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Restructure results

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commit 4783b1abb9ac1c11327ffaf90002a73de102f485 1 parent fa2dd70
Felix Stürmer authored
Showing with 845 additions and 118 deletions.
  1. +2 −0  .gitignore
  2. +46 −14 src/benchmark/clicommands/results.py
  3. +13 −2 src/benchmark/collect_results.sh
  4. +252 −0 src/benchmark/configs/l_luma_canny_1_5_pmean_g8_3_hi_1000_itf_s4_a20.config.json
  5. +252 −0 src/benchmark/configs/l_luma_canny_1_5_pmean_g8_3_hi_1000_itf_s4_a4.config.json
  6. +1 −1  thesis/chapters/chapter02/image_transformations.tex
  7. +3 −2 thesis/chapters/chapter04.tex
  8. +7 −3 thesis/chapters/chapter04/{results.tex → cross_domain_results.tex}
  9. +3 −0  thesis/chapters/chapter04/intra_domain_results.tex
  10. +11 −12 thesis/chapters/chapter04/results_local.tex
  11. +165 −0 thesis/chapters/chapter04/results_parameters.tex
  12. +5 −1 thesis/illustrations/graphics_settings.tex
  13. +2 −0  thesis/illustrations/graphs/best_performers.tex
  14. +7 −0 thesis/results/best_performers.csv
  15. +7 −7 thesis/results/g_luma_canny_mean_1.csv
  16. +7 −7 thesis/results/g_luma_mean_1.csv
  17. +5 −5 thesis/results/g_luma_segment_mean_1.csv
  18. +5 −5 thesis/results/g_luma_sobel_mean_1.csv
  19. +6 −6 thesis/results/l_luma_canny_pmean2_1.csv
  20. +6 −17 thesis/results/l_luma_canny_pmean_1.csv
  21. +6 −6 thesis/results/l_luma_pmean2_1.csv
  22. +6 −6 thesis/results/l_luma_pmean_1.csv
  23. +6 −6 thesis/results/l_luma_segment_pmean2_1.csv
  24. +6 −6 thesis/results/l_luma_segment_pmean_1.csv
  25. +6 −6 thesis/results/l_luma_sobel_pmean2_1.csv
  26. +6 −6 thesis/results/l_luma_sobel_pmean_1.csv
  27. +4 −0 thesis/results/parameter_angles.csv
  28. BIN  thesis/thesis.pdf
View
2  .gitignore
@@ -2,3 +2,5 @@
*.pyo
*.pyc
*.aux
+fcache_*
+ccache_*
View
60 src/benchmark/clicommands/results.py
@@ -14,6 +14,13 @@
"metrics.emd": "EMD",
"metrics.histogram_intersection": "HI",
"metrics.histogram_intersection_binary": "HIB",
+ "features.patch_means": "PMEAN",
+ "features.patch_means2": "PMEAN2",
+ "features.global_means": "MEAN",
+ "readers.canny": "CANNY",
+ "readers.sobel": "SOBEL",
+ "readers.segment": "SEGMENT",
+ "readers.luma": "LUMA",
}
@@ -55,6 +62,22 @@ def _format_description(self, result_info):
def take_action(self, args):
results = []
+ columns = [
+ "ConfigFilename",
+ "MeanCorrelation",
+ "StandardDeviation",
+ "Description",
+ "features",
+ "scales",
+ "angles",
+ "gridsize",
+ "patchsize",
+ "cannysigma",
+ "queryreader",
+ "imagereader",
+ "metric",
+ ]
+ sort_keys = columns[4:]
for result_filename in args.results:
result_path = pathlib.Path(result_filename)
@@ -63,24 +86,33 @@ def take_action(self, args):
config = result_info.get("config", {})
correlations_keys = sorted(result_info["correlations"].keys())
+ short_correlation_keys = [str(pathlib.Path(str(p)).parts[-1]) for p in correlations_keys]
std_dev = numpy.std(result_info["correlations"].values())
- results.append([
- str(result_path.parts[-1]).replace("_", "\_"),
- result_info["mean_correlation"],
- std_dev,
- self._format_description(result_info),
- config.get("curvelets", {}).get("scales", ""),
- config.get("curvelets", {}).get("angles", ""),
- config.get("features", {}).get("grid_size", ""),
- config.get("features", {}).get("patch_size", ""),
- config.get("readers", {}).get("canny_sigma", ""),
- TRANSLATIONS.get(config.get("metric", {}).get("metric", ""), ""),
- ] + [result_info["correlations"][p] for p in correlations_keys])
+ entry = {
+ "ConfigFilename": str(result_path.parts[-1]).replace("_", "\_"),
+ "MeanCorrelation": result_info["mean_correlation"],
+ "StandardDeviation": std_dev,
+ "Description": self._format_description(result_info),
+ "scales": config.get("curvelets", {}).get("scales", ""),
+ "angles": config.get("curvelets", {}).get("angles", ""),
+ "features": TRANSLATIONS.get(config.get("features", {}).get("extractor", ""), ""),
+ "gridsize": config.get("features", {}).get("grid_size", ""),
+ "patchsize": config.get("features", {}).get("patch_size", ""),
+ "cannysigma": config.get("readers", {}).get("canny_sigma", ""),
+ "queryreader": TRANSLATIONS.get(config.get("readers", {}).get("query", ""), ""),
+ "imagereader": TRANSLATIONS.get(config.get("readers", {}).get("image", ""), ""),
+ "metric": TRANSLATIONS.get(config.get("metric", {}).get("metric", ""), ""),
+ }
+ for s, k in zip(short_correlation_keys, correlations_keys):
+ entry[s] = result_info["correlations"][k]
+
+ results.append(entry)
+ results.sort(key=lambda x: [x[k] for k in sort_keys])
return (
- ["ConfigFilename", "MeanCorrelation", "StandardDeviation", "Description", "scales", "angles", "gridsize", "patchsize", "cannysigma", "metric"] + [str(pathlib.Path(str(p)).parts[-1]) for p in correlations_keys],
- sorted(results, key=lambda x: x[3:8]),
+ columns + short_correlation_keys,
+ [[row[c] for c in columns + short_correlation_keys] for row in results]
)
View
15 src/benchmark/collect_results.sh
@@ -13,5 +13,16 @@ eval "$CMD r_l_luma_segment_pmean_* > ${TARGET_DIR}/l_luma_segment_pmean_1.csv"
eval "$CMD r_l_luma_segment_pmean2_* > ${TARGET_DIR}/l_luma_segment_pmean2_1.csv"
eval "$CMD r_l_luma_sobel_pmean_* > ${TARGET_DIR}/l_luma_sobel_pmean_1.csv"
eval "$CMD r_l_luma_sobel_pmean2_* > ${TARGET_DIR}/l_luma_sobel_pmean2_1.csv"
-eval "$CMD r_l_luma_canny*_pmean_* > ${TARGET_DIR}/l_luma_canny_pmean_1.csv"
-eval "$CMD r_l_luma_canny*_pmean2_* > ${TARGET_DIR}/l_luma_canny_pmean2_1.csv"
+eval "$CMD r_l_luma_canny_1_5_pmean_g8_3_*_s4_a12* > ${TARGET_DIR}/l_luma_canny_pmean_1.csv"
+eval "$CMD r_l_luma_canny_1_5_pmean2_g8_3_*_s4_a12* > ${TARGET_DIR}/l_luma_canny_pmean2_1.csv"
+eval "$CMD\
+ r_g_luma_mean_g12_cos_s4_a12.*.json\
+ r_g_luma_canny_1_5_mean_g8_cos_s4_a12.*.json\
+ r_g_luma_sobel_mean_g12_cos_s4_a12.*.json\
+ r_l_luma_canny_1_5_pmean_g8_3_hi_1000_itf_s4_a12.*.json\
+ r_l_luma_canny_1_5_pmean2_g8_3_cos_1000_itf_s4_a12.*.json\
+ r_l_luma_sobel_pmean_g8_3_hibin_1000_itf_s4_a12.*.json\
+ > ${TARGET_DIR}/best_performers.csv"
+eval "$CMD\
+ r_l_luma_canny_1_5_pmean_g8_3_hi_1000_itf_s4_a*.*.json\
+ > ${TARGET_DIR}/parameter_angles.csv"
View
252 src/benchmark/configs/l_luma_canny_1_5_pmean_g8_3_hi_1000_itf_s4_a20.config.json
@@ -0,0 +1,252 @@
+{
+ "images": [
+ {
+ "query_image": "/home/laeroth/Downloads/benchmark/sketches/0.png",
+ "source_images": [
+ "/home/laeroth/Downloads/benchmark/images/0/*.jpg"
+ ],
+ "key": "set0"
+ },
+ {
+ "query_image": "/home/laeroth/Downloads/benchmark/sketches/1.png",
+ "source_images": [
+ "/home/laeroth/Downloads/benchmark/images/1/*.jpg"
+ ],
+ "key": "set1"
+ },
+ {
+ "query_image": "/home/laeroth/Downloads/benchmark/sketches/2.png",
+ "source_images": [
+ "/home/laeroth/Downloads/benchmark/images/2/*.jpg"
+ ],
+ "key": "set2"
+ },
+ {
+ "query_image": "/home/laeroth/Downloads/benchmark/sketches/4.png",
+ "source_images": [
+ "/home/laeroth/Downloads/benchmark/images/4/*.jpg"
+ ],
+ "key": "set4"
+ },
+ {
+ "query_image": "/home/laeroth/Downloads/benchmark/sketches/6.png",
+ "source_images": [
+ "/home/laeroth/Downloads/benchmark/images/6/*.jpg"
+ ],
+ "key": "set6"
+ },
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+ "query_image": "/home/laeroth/Downloads/benchmark/sketches/7.png",
+ "source_images": [
+ "/home/laeroth/Downloads/benchmark/images/7/*.jpg"
+ ],
+ "key": "set7"
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+ "query_image": "/home/laeroth/Downloads/benchmark/sketches/8.png",
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+ "/home/laeroth/Downloads/benchmark/images/8/*.jpg"
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+ "/home/laeroth/Downloads/benchmark/images/9/*.jpg"
+ ],
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+ "/home/laeroth/Downloads/benchmark/images/30/*.jpg"
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+ ],
+ "key": "set48"
+ }
+ ],
+ "curvelets": {
+ "transform": "transforms.curvelet_strictangles",
+ "angles": 20,
+ "scales": 4
+ },
+ "readers": {
+ "query": "readers.luma",
+ "image": "readers.canny",
+ "canny_sigma": 1.5
+ },
+ "features": {
+ "extractor": "features.patch_means",
+ "grid_size": 8,
+ "patch_size": 3
+ },
+ "codebook": {
+ "codebook_size": 1000,
+ "metric": "kmeans"
+ },
+ "weights": {
+ "use_weights": "weights.tfidf",
+ "use_stopwords": false
+ },
+ "metric": {
+ "metric": "metrics.histogram_intersection"
+ },
+ "cache": {
+ "feature_enabled": true,
+ "reader_enabled": true,
+ "reader_path": "/media/data0/unsorted/caches"
+ }
+}
View
252 src/benchmark/configs/l_luma_canny_1_5_pmean_g8_3_hi_1000_itf_s4_a4.config.json
@@ -0,0 +1,252 @@
+{
+ "images": [
+ {
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+ "source_images": [
+ "/home/laeroth/Downloads/benchmark/images/0/*.jpg"
+ ],
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+ ],
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+ },
+ {
+ "query_image": "/home/laeroth/Downloads/benchmark/sketches/47.png",
+ "source_images": [
+ "/home/laeroth/Downloads/benchmark/images/47/*.jpg"
+ ],
+ "key": "set47"
+ },
+ {
+ "query_image": "/home/laeroth/Downloads/benchmark/sketches/48.png",
+ "source_images": [
+ "/home/laeroth/Downloads/benchmark/images/48/*.jpg"
+ ],
+ "key": "set48"
+ }
+ ],
+ "curvelets": {
+ "transform": "transforms.curvelet_strictangles",
+ "angles": 4,
+ "scales": 4
+ },
+ "readers": {
+ "query": "readers.luma",
+ "image": "readers.canny",
+ "canny_sigma": 1.5
+ },
+ "features": {
+ "extractor": "features.patch_means",
+ "grid_size": 8,
+ "patch_size": 3
+ },
+ "codebook": {
+ "codebook_size": 1000,
+ "metric": "kmeans"
+ },
+ "weights": {
+ "use_weights": "weights.tfidf",
+ "use_stopwords": false
+ },
+ "metric": {
+ "metric": "metrics.histogram_intersection"
+ },
+ "cache": {
+ "feature_enabled": true,
+ "reader_enabled": true,
+ "reader_path": "/media/data0/unsorted/caches"
+ }
+}
View
2  thesis/chapters/chapter02/image_transformations.tex
@@ -175,7 +175,7 @@ \subsection{The Continuous Curvelet Transform}\label{sec:background_cct}
Note that, in contrast to the Gabor wavelets (Figure \ref{fig:gabor_tiling}),
there is no gap in between the curvelets, so no information is lost.
-\subsection{The Fast Discrete Curvelet Transform}
+\subsection{The Fast Discrete Curvelet Transform}\label{sec:background_fdct}
Based on the above definition of the continuous curvelet transform, a team
around the authors of the original curvelet publication presented two digital,
View
5 thesis/chapters/chapter04.tex
@@ -1,6 +1,6 @@
\chapter{Experimental Results}\label{ch:results}
-This chapter will present the benchmarking method as well as the specific
+This chapter will present the benchmarking methods as well as the specific
processing pipelines constructed from the steps explained in chapter
\ref{ch:solution}. A detailed description of the experimental results for each
pipeline variation will follow. Many pipelines were tested with varying
@@ -8,4 +8,5 @@ \chapter{Experimental Results}\label{ch:results}
specific to the implementation.
\input{chapters/chapter04/benchmarking.tex}
-\input{chapters/chapter04/results.tex}
+\input{chapters/chapter04/cross_domain_results.tex}
+\input{chapters/chapter04/intra_domain_results.tex}
View
10 thesis/chapters/chapter04/results.tex → thesis/chapters/chapter04/cross_domain_results.tex
@@ -1,7 +1,7 @@
-\section{Variants and Results}\label{sec:results}
+\section{Cross-Domain Results}\label{sec:cross_results}
Some of the variations below include a Canny edge detector as a preprocessing
-step. The parameter $\sigma$ determines the size of the gaussian kernel used by
+step. The parameter $\sigma$ determines the size of the Gaussian kernel used by
the Canny algorithm. A value of $\sigma = 1.5$ was determined to be appropriate
for the image dataset used in the benchmark. Value larger than $\sigma = 2$
tend not to detect any edges, while smaller values produced more "false" edges
@@ -31,7 +31,11 @@ \section{Variants and Results}\label{sec:results}
red line denotes the best result obtained by Eitz et al.\ in
\autocite{eitz_sketch-based_2010} using the SHOG descriptor.
+The first two sections present the results grouped by preprocessing steps and
+sampling method. Afterwards, the influence of parameter variations on selected
+pipelines are examined.
+
\input{chapters/chapter04/results_global.tex}
\input{chapters/chapter04/results_local.tex}
-
+\input{chapters/chapter04/results_parameters.tex}
View
3  thesis/chapters/chapter04/intra_domain_results.tex
@@ -0,0 +1,3 @@
+\section{Intra-Domain Results}\label{sec:intra_results}
+
+TBD
View
23 thesis/chapters/chapter04/results_local.tex
@@ -55,16 +55,11 @@ \subsection{Local Features}
Using the Canny edge detector and PMEAN sampling
(Figure~\ref{fig:pipeline_local_luma_canny_pmean}), the rank correlation
coefficient for COS and HI exceed all previous results
-(Table~\ref{tab:results_local_luma_canny_pmean}). In an attempt to improve the
-result further, several sets of parameter values are examined: For the number
-of angles $N_{\theta} \in \{8, 12, 16\}$ the differences are small with
-$N_{\theta} = 12$ yielding the highest results. For the grid and patch sizes of
-the best results, $\frac{P}{G} \approx \frac{1}{3}$ seems to hold.
+(Table~\ref{tab:results_local_luma_canny_pmean}) with the highest value
+achieved is $0.22$. Treating the coefficients on different scales separately
+using the PMEAN2 sampling method is inferior to PMEAN in this case.
-Treating the coefficients on different scales separately using the PMEAN2
-sampling method is inferior to PMEAN in this case.
-
-\begin{figure}[h!]
+\begin{figure}[h]
\centering
\input{illustrations/pipelines/local_luma_canny_pmean.tex}
\caption[Local CANNY+PMEAN Pipelines]{
@@ -73,7 +68,7 @@ \subsection{Local Features}
\label{fig:pipeline_local_luma_canny_pmean}
\end{figure}
-\begin{table}[h!]
+\begin{table}[h]
\centering
\subfloat[Local CANNY+PMEAN Results]{%
\input{illustrations/graphs/l_luma_canny_pmean.tex}
@@ -94,7 +89,9 @@ \subsection{Local Features}
\paragraph{SOBEL+PMEAN(2)}
%\subsubsection{SOBEL+PMEAN(2)}
-foo
+Preprocessing the database images with the Sobel operator results in slightly
+lower correlation coefficients. The PMEAN2 sampling algorithm yields better
+results for the $L_2$ and EMD metrics, but worse for COS, HI and HIB.
\begin{figure}[h]
\centering
@@ -125,7 +122,9 @@ \subsection{Local Features}
\FloatBarrier
\paragraph{SEGMENT+PMEAN(2)}
-foo
+The SEGMENT step leads to similar, although slightly lower, results than the
+Sobel operator. Neither PMEAN nor PMEAN2 have a consistent advantage across the
+different distance measures.
\begin{figure}[h]
\centering
View
165 thesis/chapters/chapter04/results_parameters.tex
@@ -0,0 +1,165 @@
+\subsection{Parameter Variations}
+
+In an attempt to improve the results, the best performing configurations
+presented in the previous sections will be re-used with varying parameter
+values (Table~\ref{tab:results_best_performers}). The following sections
+compare the results of changing the parameters $N_{\theta}$, $P$, $G$ and
+$\sigma$.
+
+\begin{table}[h]
+ \centering
+ \pgfplotstableread[]{results/best_performers.csv}\resultsbestperformers
+ \plottablexbars{imagereader,features,gridsize,patchsize,cannysigma,metric}{\resultsbestperformers}
+ \caption[Best Performing Configurations]{
+ Best Performing Configurations with default assumptions $N_s=4$ and
+ $N_{\theta}=12$.
+ }
+ \label{tab:results_best_performers}
+\end{table}
+
+\FloatBarrier
+\subsubsection{Curvelet Angles}
+
+The parameter $N_{\theta}$ controls the number of angles, the curvelet
+coronization is divided into (see Section~\ref{sec:background_fdct}).
+Therefore, it determines how finely the angles of the lines are resolved and
+how sensitive to angular differences the descriptor is.
+
+\begin{table}[h]
+ \centering
+ \pgfplotstableread[]{results/parameter_angles.csv}\resultsparameterangles
+ \plottablexbars{scales,angles,cannysigma,metric}{\resultsparameterangles}
+ \caption[Angle Parameter Results]{
+ Influence of $N_{\theta}$ on the results of CANNY+PMEAN for $G=8$,
+ $P=3$ and $\sigma=1.5$.
+ }
+ \label{tab:results_best_performers}
+\end{table}
+
+\FloatBarrier
+%In an attempt to improve the
+%result further, several sets of parameter values are examined: For the number
+%of angles $N_{\theta} \in \{8, 12, 16\}$ the differences are small with
+%$N_{\theta} = 12$ yielding the highest results. For the grid and patch sizes of
+%the best results, $\frac{P}{G} \approx \frac{1}{3}$ seems to hold.
+
+
+%The processing steps applied to the database images and the query images in the
+%pipelines based on global features are almost identical, with the exception of
+%the CANNY, SOBEL and SEGMENT steps for the database images. The curvelet
+%responses are averaged on a grid and the features are ranked using the $L_2$
+%and the COS distance measures.
+
+%\paragraph{LUMA+MEAN}
+
+%The most straightforward combination of processing steps consists of a LUMA
+%input image, on which the means of $G \times G$ grid cells is computed for each
+%scale and angle (Figure \ref{fig:pipeline_global_luma_mean}). Varying $G$
+%determines the feature size that can be encoded best. Setting $G=12$ seems to
+%yield the best correlation, although the advantage over $G=8$ and $G=16$ is
+%below $0.01$. The distance measure COS outperforms the $L_2$ measure (Table
+%\ref{tab:results_global_luma_mean}). A possible explanation would be that the
+%COS measure normalizes the magnitude of the feature vector as discussed in
+%\ref{sec:anatomy_ranking_distance_euclidean}.
+
+%\begin{figure}[h]
+ %\centering
+ %\input{illustrations/pipelines/global_luma_mean.tex}
+ %\caption[Global LUMA+MEAN Pipelines]{
+ %Global LUMA+MEAN Pipelines
+ %}
+ %\label{fig:pipeline_global_luma_mean}
+%\end{figure}
+
+%\begin{table}[h]
+ %\centering
+ %\input{illustrations/graphs/g_luma_mean.tex}
+ %\caption[Global LUMA+MEAN Results]{
+ %Global LUMA+MEAN Results
+ %}
+ %\label{tab:results_global_luma_mean}
+%\end{table}
+
+%\FloatBarrier
+%\paragraph{CANNY+MEAN}
+
+%In addition to reading the images like in the previous LUMA+MEAN configuration,
+%this pipeline applies a CANNY processing step to the database images in an
+%attempt to bring the query and database image domains closer together (Figure
+%\ref{fig:pipeline_global_luma_canny_mean}). Again, the COS distance measure
+%produces the best rankings (Table~\ref{tab:results_global_luma_canny_mean}).
+%Surprisingly, the Canny edge detector does not lead to increased performance in
+%comparison to plain the LUMA preprocessing step.
+
+%\begin{figure}[h]
+ %\centering
+ %\input{illustrations/pipelines/global_luma_canny_mean.tex}
+ %\caption[Global CANNY+MEAN Pipelines]{
+ %Global CANNY+MEAN Pipelines
+ %}
+ %\label{fig:pipeline_global_luma_canny_mean}
+%\end{figure}
+
+%\begin{table}[h]
+ %\centering
+ %\input{illustrations/graphs/g_luma_canny_mean.tex}
+ %\caption[Global CANNY+MEAN Results]{
+ %Global CANNY+MEAN Results
+ %}
+ %\label{tab:results_global_luma_canny_mean}
+%\end{table}
+
+%\FloatBarrier
+%\paragraph{SOBEL+MEAN}
+
+%The SOBEL step used in this variant also attempts to bring the database images
+%into the sketch domain (Figure~\ref{fig:pipeline_global_luma_sobel_mean}). The
+%results are slightly better than with the CANNY preprocessor
+%(Table~\ref{tab:results_global_luma_sobel_mean}).
+
+%\begin{figure}[h!]
+ %\centering
+ %\input{illustrations/pipelines/global_luma_sobel_mean.tex}
+ %\caption[Global SOBEL+MEAN Pipelines]{
+ %Global SOBEL+MEAN Pipelines
+ %}
+ %\label{fig:pipeline_global_luma_sobel_mean}
+%\end{figure}
+
+%\begin{table}[h!]
+ %\centering
+ %\input{illustrations/graphs/g_luma_sobel_mean.tex}
+ %\caption[Global SOBEL+MEAN Results]{
+ %Global SOBEL+MEAN Results
+ %}
+ %\label{tab:results_global_luma_sobel_mean}
+%\end{table}
+
+%\FloatBarrier
+%\paragraph{SEGMENT+MEAN}
+
+%With the gPb contour detector in the SEGMENT step to find edges in the database
+%images (Figure~\ref{fig:pipeline_global_luma_segment_mean}), the $L_2$ distance
+%metric produces results comparable to the COS metric in the CANNY+MEAN variant
+%(Table~\ref{tab:results_global_luma_segment_mean}). Unlike in the other cases,
+%the COS distance measure performs worse than the $L_2$ metric.
+
+%\begin{figure}[h]
+ %\centering
+ %\input{illustrations/pipelines/global_luma_segment_mean.tex}
+ %\caption[Global SEGMENT+MEAN Pipelines]{
+ %Global SEGMENT+MEAN Pipelines
+ %}
+ %\label{fig:pipeline_global_luma_segment_mean}
+%\end{figure}
+
+%\begin{table}[h]
+ %\centering
+ %\input{illustrations/graphs/g_luma_segment_mean.tex}
+ %\caption[Global SEGMENT+MEAN Results]{
+ %Global SEGMENT+MEAN Results
+ %}
+ %\label{tab:results_global_luma_segment_mean}
+%\end{table}
+
+%\FloatBarrier
View
6 thesis/illustrations/graphics_settings.tex
@@ -35,6 +35,7 @@
},
hbarplot/.style={
xbar,
+ %xcomb,
small,
y=-\baselineskip,
enlarge y limits={true, abs value=0.45},
@@ -62,7 +63,7 @@
\newcommand{\addreferenceplot}{\addplot[const plot, red, update limits=false] table[x=MeanCorrelation, y expr=\coordindex*100-1] {\resultsreference}}
\newcommand{\plotxbars}[1]{%
- \begin{tikzpicture}
+ \begin{tikzpicture}[trim axis left, trim axis right]
\begin{axis}[
hbarplot,
width=6cm,
@@ -88,6 +89,9 @@
columns/patchsize/.style={string type,column name=$P$},
columns/cannysigma/.style={string type,column name=$\sigma$},
columns/metric/.style={string type,column name=Metric},
+ columns/queryreader/.style={string type,column name=Preproc.},
+ columns/imagereader/.style={string type,column name=Preproc.},
+ columns/features/.style={string type,column name=Sampling},
columns/graph/.style={
column name={},
assign cell content/.code={% use \multirow for Z column:
View
2  thesis/illustrations/graphs/best_performers.tex
@@ -0,0 +1,2 @@
+\pgfplotstableread[]{results/g_luma_mean_1.csv}\resultsglumamean
+\plottablexbars{scales,angles,gridsize,metric}{\resultsglumamean}
View
7 thesis/results/best_performers.csv
@@ -0,0 +1,7 @@
+{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {features} {scales} {angles} {gridsize} {patchsize} {cannysigma} {queryreader} {imagereader} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
+{r\_g\_luma\_canny\_1\_5\_mean\_g8\_cos\_s4\_a12.2012-09-26T17:15:49.369924.json} {0.187587640166} {0.170881204229} {$\sigma = 1.5$, $N = (4, 12)$, $G=8$, COS} {MEAN} {4} {12} {8} {} {1.5} {LUMA} {CANNY} {COS} {0.387640769749} {0.0244690186885} {0.215348564326} {0.26143214704} {0.189558317101} {0.286448829729} {0.147720248515} {0.12355304448} {0.265467444325} {0.185569475644} {0.407719149514} {-0.160841049464} {0.112187575427} {0.412863870643} {0.29086445888} {0.333983701559} {0.290517331834} {0.194213222639} {0.116741524978} {-0.0206455909637} {0.0386101665159} {-0.282584409058} {0.0964635211783} {0.0771210774486} {0.469078396768} {0.376293658946} {0.245166392694} {0.0516135906955} {0.460749895039} {0.0541244303963} {0.163768069876}
+{r\_g\_luma\_mean\_g12\_cos\_s4\_a12.2012-09-26T17:36:54.769566.json} {0.191010459445} {0.152000213839} {$N = (4, 12)$, $G=12$, COS} {MEAN} {4} {12} {12} {} {} {LUMA} {LUMA} {COS} {0.385065083571} {0.0862854869541} {0.0477119573656} {0.351581163261} {0.143135872097} {0.273603590727} {0.191394061119} {0.0823686963199} {0.141754460562} {0.198456244786} {0.371706101607} {-0.108956839959} {0.233401737383} {0.263666957887} {0.29086445888} {0.362352973506} {0.342164857494} {0.304824726923} {0.0577293255387} {0.0} {0.236809021297} {-0.249035575106} {0.114470045132} {0.118252318755} {0.391757781916} {0.237116552212} {0.296780370103} {0.0490329111607} {0.422139568639} {0.141754460562} {0.143135872097}
+{r\_g\_luma\_sobel\_mean\_g12\_cos\_s4\_a12.2012-09-26T18:12:57.759748.json} {0.200316890204} {0.144855610681} {$N = (4, 12)$, $G=12$, COS} {MEAN} {4} {12} {12} {} {} {LUMA} {SOBEL} {COS} {0.372186652682} {0.0682556837099} {0.166347094599} {0.343854104727} {0.153451970987} {0.242775017124} {0.209377395721} {0.0720726092799} {0.2087656601} {0.206188306272} {0.38971262556} {-0.0933915771081} {0.26177100933} {0.2919629241} {0.28829043712} {0.344299800449} {0.318923470947} {0.343410135395} {0.0962155425645} {0.0516139774093} {0.213642921388} {-0.212906061619} {0.106752963437} {0.133676534244} {0.35309747449} {0.268044798153} {0.252908489305} {0.00774203860432} {0.468471960319} {0.14433181439} {0.137977822652}
+{r\_l\_luma\_canny\_1\_5\_pmean\_g8\_3\_hi\_1000\_itf\_s4\_a12.2012-09-25T11:53:09.327659.json} {0.220203568362} {0.168425877376} {$\sigma = 1.5$, $N = (4, 12)$, $G = (8, 3)$, HI} {PMEAN} {4} {12} {8} {3} {1.5} {LUMA} {CANNY} {HI} {0.32006761566} {-0.00257733884789} {0.483294340369} {0.415186544765} {0.125729125562} {0.560289678293} {0.128461993571} {0.2786126201} {0.169193460083} {0.165910618692} {0.38059276111} {-0.124332951727} {0.298119063217} {0.384240799344} {0.301695585518} {0.306594152686} {0.0649483104381} {0.0411741950684} {0.145140456935} {0.246900945644} {0.364254908059} {-0.171513005379} {0.268593489298} {0.109161399621} {0.178370555847} {0.282401328761} {0.122898726332} {0.0402291503823} {0.448188032527} {0.131512200427} {0.362971856879}
+{r\_l\_luma\_sobel\_pmean\_g8\_3\_hibin\_1000\_itf\_s4\_a12.2012-09-28T14:52:43.103812.json} {0.186833698667} {0.174668901699} {$N = (4, 12)$, $G = (8, 3)$, HIB} {PMEAN} {4} {12} {8} {3} {} {LUMA} {SOBEL} {HIB} {0.207991927493} {-0.00647249326515} {0.12543246251} {0.498803375818} {-0.0498061453641} {0.502385927389} {0.170636738678} {0.164766297575} {0.156972890746} {0.106040489335} {0.412726052698} {-0.0284703432478} {0.277846203751} {0.412345735869} {0.495787135666} {0.351570311078} {-0.0103494270504} {0.0194682100329} {0.221691255049} {0.0310881872273} {0.38747471875} {0.0236393970833} {0.161253282856} {0.112168833957} {0.258518851151} {0.107792571404} {-0.0104992663074} {-0.0224760391403} {0.322624338286} {-0.011695968159} {0.402589146816}
+{r\_l\_luma\_canny\_1\_5\_pmean2\_g8\_3\_cos\_1000\_itf\_s4\_a12.2012-09-28T15:11:59.054471.json} {0.190418381806} {0.169301638911} {$\sigma = 1.5$, $N = (4, 12)$, $G = (8, 3)$, COS} {PMEAN2} {4} {12} {8} {3} {1.5} {LUMA} {CANNY} {COS} {0.280628974754} {0.0773778311648} {0.422786161718} {0.347242923307} {0.207293648568} {0.4323109704} {0.231554825893} {0.255046173931} {0.106667026482} {0.0904952271363} {0.393066418095} {-0.160793941617} {0.312103597331} {0.323528928756} {0.291675145719} {0.317890753405} {-0.0125898873316} {0.0606241902996} {0.286172235698} {0.233963929973} {0.256092309173} {-0.189305358451} {0.371476337884} {0.176189818224} {-0.0808394354329} {0.0870860951352} {0.0180320915358} {-0.03921999302} {0.363275754073} {0.0896601283118} {0.353476954879}
View
14 thesis/results/g_luma_canny_mean_1.csv
@@ -1,7 +1,7 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
-{r\_g\_luma\_canny\_1\_5\_mean\_g12\_l2\_s4\_a12.2012-09-25T19:17:22.186931.json} {0.0957932434303} {0.158421368032} {$\sigma = 1.5$, $N = (4, 12)$, $G=12$, $L_2$} {4} {12} {12} {} {1.5} {$L_2$} {0.158404699931} {0.130072151976} {0.26177100933} {0.248553716151} {0.220506613771} {0.281310734128} {-0.0372511931037} {-0.02316619584} {-0.0386603074259} {0.134022399077} {0.119614766261} {-0.251638416097} {0.109608550705} {0.317686529747} {0.17760750144} {0.434565665735} {-0.0710153477817} {0.0707399155307} {0.0064143695043} {0.0800016649843} {0.087516377436} {0.210325382084} {-0.168489616991} {0.0899745903567} {-0.113403568449} {0.0412376612543} {0.234843597212} {-0.141937374413} {0.35521500288} {-0.0412376612543} {0.0863973282025}
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+{r\_g\_luma\_canny\_1\_5\_mean\_g12\_l2\_s4\_a12.2012-09-25T19:17:22.186931.json} {0.0957932434303} {0.158421368032} {$\sigma = 1.5$, $N = (4, 12)$, $G=12$, $L_2$} {MEAN} {4} {12} {12} {} {1.5} {LUMA} {CANNY} {$L_2$} {0.158404699931} {0.130072151976} {0.26177100933} {0.248553716151} {0.220506613771} {0.281310734128} {-0.0372511931037} {-0.02316619584} {-0.0386603074259} {0.134022399077} {0.119614766261} {-0.251638416097} {0.109608550705} {0.317686529747} {0.17760750144} {0.434565665735} {-0.0710153477817} {0.0707399155307} {0.0064143695043} {0.0800016649843} {0.087516377436} {0.210325382084} {-0.168489616991} {0.0899745903567} {-0.113403568449} {0.0412376612543} {0.234843597212} {-0.141937374413} {0.35521500288} {-0.0412376612543} {0.0863973282025}
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+{r\_g\_luma\_canny\_1\_5\_mean\_g16\_cos\_s4\_a12.2012-09-27T15:36:10.675572.json} {0.182302241647} {0.157150725707} {$\sigma = 1.5$, $N = (4, 12)$, $G=16$, COS} {MEAN} {4} {12} {16} {} {1.5} {LUMA} {CANNY} {COS} {0.369610966505} {0.0038635292666} {0.150872946264} {0.235675285263} {0.132819773207} {0.242775017124} {0.186255965519} {0.17245945792} {0.265467444325} {0.252580675183} {0.469455803068} {-0.0311305257027} {0.156030995709} {0.343410135395} {0.22908793664} {0.212769539603} {0.331835352362} {0.196785583204} {0.0859525513576} {0.0154841932228} {0.0694982997286} {-0.326455961149} {0.140193650779} {0.0359898361427} {0.438150150827} {0.440727504656} {0.141938437875} {0.0593556292998} {0.2960125024} {0.134022399077} {0.199874415991}
View
14 thesis/results/g_luma_mean_1.csv
@@ -1,7 +1,7 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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-{r\_g\_luma\_mean\_g16\_cos\_s4\_a12.2012-09-26T18:16:09.392254.json} {0.186288185326} {0.14571078523} {$N = (4, 12)$, $G=16$, COS} {4} {12} {16} {} {} {COS} {0.400519200637} {0.0940125454873} {0.0373958584757} {0.320672929128} {0.143135872097} {0.242775017124} {0.19910120452} {0.0720726092799} {0.167527998846} {0.213920367757} {0.371706101607} {-0.111551050435} {0.246296860995} {0.253377515628} {0.2445320672} {0.370090047673} {0.362823867757} {0.299680005794} {0.0295060997198} {0.0490332785388} {0.259975121207} {-0.25677761371} {0.127331847955} {0.09511599552} {0.378871012774} {0.25773538284} {0.240004994953} {0.0825817451128} {0.30630858944} {0.134022399077} {0.143135872097}
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+{r\_g\_luma\_mean\_g8\_l2\_s4\_a12.2012-09-25T15:11:16.838831.json} {0.139733804686} {0.176534295721} {$N = (4, 12)$, $G=8$, $L_2$} {MEAN} {4} {12} {8} {} {} {LUMA} {LUMA} {$L_2$} {0.287189008817} {0.12492077962} {0.372669072396} {0.307794498239} {0.393301270176} {0.235067873724} {-0.00385357170039} {0.03346228288} {0.185569475644} {-0.0927847378222} {0.281673481841} {-0.313899467502} {0.132819773207} {0.217364467722} {0.1801815232} {0.452618838792} {0.262111192721} {0.327975972006} {0.0474663343318} {0.067098170632} {0.350065509744} {0.0554846099976} {-0.0450163098832} {0.146530047152} {-0.0927847378222} {-0.0644338457099} {0.286457574621} {-0.116130579065} {0.2445320672} {0.0721659071951} {-0.00386853708369}
+{r\_g\_luma\_mean\_g8\_cos\_s4\_a12.2012-09-26T16:33:42.215836.json} {0.182697558308} {0.162052888389} {$N = (4, 12)$, $G=8$, COS} {MEAN} {4} {12} {8} {} {} {LUMA} {LUMA} {COS} {0.421124690059} {0.114618034909} {0.101871476537} {0.392792142104} {0.150872946264} {0.291586925329} {0.0578035755058} {0.0566284787199} {0.100516799307} {0.108248860793} {0.345982495959} {-0.127116313286} {0.194716366546} {0.309969448053} {0.28056837184} {0.437144690457} {0.303429213249} {0.281673481841} {0.0679923167456} {-0.00774209661139} {0.244531054601} {-0.259358293245} {0.0964635211783} {0.128535129081} {0.373716305117} {0.219075075414} {0.237424296083} {0.0645169883694} {0.465897938559} {0.0773206148519} {0.132819773207}
+{r\_g\_luma\_mean\_g12\_l2\_s4\_a12.2012-09-26T18:09:49.860088.json} {0.140398599241} {0.175844680858} {$N = (4, 12)$, $G=12$, $L_2$} {MEAN} {4} {12} {12} {} {} {LUMA} {LUMA} {$L_2$} {0.251129402329} {0.117193721087} {0.380406146563} {0.300067439706} {0.406196393788} {0.209377395721} {0.0629416711063} {0.0386103264} {0.154641229704} {-0.0876300301654} {0.304824726923} {-0.316493677978} {0.143135872097} {0.17877905925} {0.17245945792} {0.442302739902} {0.290517331834} {0.34083777483} {0.0705580645473} {0.0954858582071} {0.326899409834} {0.0477425713933} {-0.0604504732717} {0.154242154897} {-0.0927847378222} {-0.085052676337} {0.320006659937} {-0.1187112586} {0.22136587136} {0.0927847378222} {-0.00902658652862}
+{r\_g\_luma\_mean\_g12\_cos\_s4\_a12.2012-09-26T17:36:54.769566.json} {0.191010459445} {0.152000213839} {$N = (4, 12)$, $G=12$, COS} {MEAN} {4} {12} {12} {} {} {LUMA} {LUMA} {COS} {0.385065083571} {0.0862854869541} {0.0477119573656} {0.351581163261} {0.143135872097} {0.273603590727} {0.191394061119} {0.0823686963199} {0.141754460562} {0.198456244786} {0.371706101607} {-0.108956839959} {0.233401737383} {0.263666957887} {0.29086445888} {0.362352973506} {0.342164857494} {0.304824726923} {0.0577293255387} {0.0} {0.236809021297} {-0.249035575106} {0.114470045132} {0.118252318755} {0.391757781916} {0.237116552212} {0.296780370103} {0.0490329111607} {0.422139568639} {0.141754460562} {0.143135872097}
+{r\_g\_luma\_mean\_g16\_cos\_s4\_a12.2012-09-26T18:16:09.392254.json} {0.186288185326} {0.14571078523} {$N = (4, 12)$, $G=16$, COS} {MEAN} {4} {12} {16} {} {} {LUMA} {LUMA} {COS} {0.400519200637} {0.0940125454873} {0.0373958584757} {0.320672929128} {0.143135872097} {0.242775017124} {0.19910120452} {0.0720726092799} {0.167527998846} {0.213920367757} {0.371706101607} {-0.111551050435} {0.246296860995} {0.253377515628} {0.2445320672} {0.370090047673} {0.362823867757} {0.299680005794} {0.0295060997198} {0.0490332785388} {0.259975121207} {-0.25677761371} {0.127331847955} {0.09511599552} {0.378871012774} {0.25773538284} {0.240004994953} {0.0825817451128} {0.30630858944} {0.134022399077} {0.143135872097}
View
10 thesis/results/g_luma_segment_mean_1.csv
@@ -1,5 +1,5 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
-{r\_g\_luma\_segment\_mean\_g4\_l2\_s4\_a12.2012-09-27T19:44:57.124732.json} {0.187650126895} {0.170388941644} {$N = (4, 12)$, $G=4$, $L_2$} {4} {12} {4} {} {} {$L_2$} {0.333551360016} {-0.104315290198} {0.0373958584757} {0.0167419601553} {0.0915553776474} {0.278741686328} {0.420039315342} {0.21106978432} {0.347942766833} {0.265467444325} {0.230226270546} {0.181594733266} {0.352036874616} {0.317686529747} {0.18790358848} {0.442302739902} {0.192387033081} {0.0424439493184} {0.247594662866} {0.358717142994} {0.298585287723} {-0.378069551844} {0.132476569085} {0.200514801366} {-0.0927847378222} {0.0360829535975} {0.16516472771} {0.252906594408} {0.25482815424} {0.149486522047} {0.346878825171}
-{r\_g\_luma\_segment\_mean\_g4\_cos\_s4\_a12.2012-09-27T19:35:03.627799.json} {0.167694342623} {0.169493865345} {$N = (4, 12)$, $G=4$, COS} {4} {12} {4} {} {} {COS} {0.240826657618} {0.279461950284} {0.166347094599} {0.163556072286} {0.0399748831982} {0.276172638528} {0.211946443521} {0.0952388051199} {0.311859813236} {0.414953966372} {0.196785583204} {0.11414526091} {0.210190514881} {0.114470045132} {0.21621782784} {0.359773948784} {0.143321883705} {-0.0398715887537} {0.13470175959} {0.27871547801} {0.293437265521} {-0.331617320218} {0.0244374253652} {0.277635878815} {0.226807136899} {0.262890090496} {0.121292846912} {-0.141937374413} {0.535396526079} {-0.15979593736} {0.161189045154}
-{r\_g\_luma\_segment\_mean\_g8\_l2\_s4\_a12.2012-09-27T19:26:24.446530.json} {0.171152394877} {0.166206340831} {$N = (4, 12)$, $G=8$, $L_2$} {4} {12} {8} {} {} {$L_2$} {0.361883907971} {-0.117193721087} {-0.0245007348634} {0.0347717633994} {0.0786602540351} {0.232498825923} {0.327553594533} {0.15958934912} {0.376293658946} {0.252580675183} {0.160772535297} {0.246449995146} {0.362352973506} {0.199357943768} {0.13642315328} {0.434565665735} {0.127827626007} {0.0372992281889} {0.221937184849} {0.283876875751} {0.154440666063} {-0.388392269984} {0.137621290214} {0.218509719438} {-0.097939445479} {0.0927847378222} {0.252908489305} {0.278713389756} {0.24968011072} {0.182992121816} {0.331404676836}
-{r\_g\_luma\_segment\_mean\_g8\_cos\_s4\_a12.2012-09-27T19:17:00.614976.json} {0.169312992894} {0.173339912176} {$N = (4, 12)$, $G=8$, COS} {4} {12} {8} {} {} {COS} {0.240826657618} {0.287189008817} {0.184400267656} {0.202191364952} {0.0709231798677} {0.229929778123} {0.142582152914} {0.02831423936} {0.355674828319} {0.484542519738} {0.235370991675} {0.163435259939} {0.349457849894} {0.00643090141189} {0.15958934912} {0.411354443233} {0.042609208669} {-0.047588670448} {0.157793489806} {0.258069887046} {0.115830499548} {-0.313552563475} {0.0527333915775} {0.30077220205} {0.126290337591} {0.309282459407} {0.263231284787} {-0.123872617669} {0.486490112639} {-0.0876300301654} {0.156030995709}
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+{r\_g\_luma\_segment\_mean\_g4\_cos\_s4\_a12.2012-09-27T19:35:03.627799.json} {0.167694342623} {0.169493865345} {$N = (4, 12)$, $G=4$, COS} {MEAN} {4} {12} {4} {} {} {LUMA} {SEGMENT} {COS} {0.240826657618} {0.279461950284} {0.166347094599} {0.163556072286} {0.0399748831982} {0.276172638528} {0.211946443521} {0.0952388051199} {0.311859813236} {0.414953966372} {0.196785583204} {0.11414526091} {0.210190514881} {0.114470045132} {0.21621782784} {0.359773948784} {0.143321883705} {-0.0398715887537} {0.13470175959} {0.27871547801} {0.293437265521} {-0.331617320218} {0.0244374253652} {0.277635878815} {0.226807136899} {0.262890090496} {0.121292846912} {-0.141937374413} {0.535396526079} {-0.15979593736} {0.161189045154}
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+{r\_g\_luma\_segment\_mean\_g8\_cos\_s4\_a12.2012-09-27T19:17:00.614976.json} {0.169312992894} {0.173339912176} {$N = (4, 12)$, $G=8$, COS} {MEAN} {4} {12} {8} {} {} {LUMA} {SEGMENT} {COS} {0.240826657618} {0.287189008817} {0.184400267656} {0.202191364952} {0.0709231798677} {0.229929778123} {0.142582152914} {0.02831423936} {0.355674828319} {0.484542519738} {0.235370991675} {0.163435259939} {0.349457849894} {0.00643090141189} {0.15958934912} {0.411354443233} {0.042609208669} {-0.047588670448} {0.157793489806} {0.258069887046} {0.115830499548} {-0.313552563475} {0.0527333915775} {0.30077220205} {0.126290337591} {0.309282459407} {0.263231284787} {-0.123872617669} {0.486490112639} {-0.0876300301654} {0.156030995709}
View
10 thesis/results/g_luma_sobel_mean_1.csv
@@ -1,5 +1,5 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
-{r\_g\_luma\_sobel\_mean\_g12\_l2\_s4\_a12.2012-09-26T15:52:10.742035.json} {0.152108185969} {0.173883531977} {$N = (4, 12)$, $G=12$, $L_2$} {4} {12} {12} {} {} {$L_2$} {0.256280774684} {0.145526269042} {0.426828591568} {0.312945870595} {0.437144690457} {0.150289296315} {0.114322627111} {0.0} {0.262890090496} {-0.0927847378222} {0.325403611441} {-0.277580520849} {0.192137341823} {0.194213222639} {0.2059217408} {0.401038344343} {0.228540301043} {0.358844298783} {0.111610029375} {0.0516139774093} {0.29086325442} {0.0529039304629} {0.0244374253652} {0.182519883295} {-0.0592791380531} {-0.0876300301654} {0.309683864456} {-0.121291938134} {0.22908793664} {0.15979593736} {-0.0709231798677}
-{r\_g\_luma\_sobel\_mean\_g12\_cos\_s4\_a12.2012-09-26T18:12:57.759748.json} {0.200316890204} {0.144855610681} {$N = (4, 12)$, $G=12$, COS} {4} {12} {12} {} {} {COS} {0.372186652682} {0.0682556837099} {0.166347094599} {0.343854104727} {0.153451970987} {0.242775017124} {0.209377395721} {0.0720726092799} {0.2087656601} {0.206188306272} {0.38971262556} {-0.0933915771081} {0.26177100933} {0.2919629241} {0.28829043712} {0.344299800449} {0.318923470947} {0.343410135395} {0.0962155425645} {0.0516139774093} {0.213642921388} {-0.212906061619} {0.106752963437} {0.133676534244} {0.35309747449} {0.268044798153} {0.252908489305} {0.00774203860432} {0.468471960319} {0.14433181439} {0.137977822652}
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12 thesis/results/l_luma_canny_pmean2_1.csv
@@ -1,6 +1,6 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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-{r\_l\_luma\_canny\_1\_5\_pmean2\_g8\_3\_hi\_1000\_itf\_s4\_a12.2012-09-28T11:48:36.906752.json} {0.185619867266} {0.176092707494} {$\sigma = 1.5$, $N = (4, 12)$, $G = (8, 3)$, HI} {4} {12} {8} {3} {1.5} {HI} {0.289292100471} {0.0800928427846} {0.396742655495} {0.398266373262} {0.0915352048464} {0.463515029517} {0.186138807011} {0.274052125532} {0.121995573708} {0.109850735306} {0.384459124268} {-0.163886132802} {0.344570757056} {0.342264192205} {0.313381203074} {0.301329157878} {-0.0288127443904} {0.0560046492474} {0.24487261266} {0.231351276648} {0.247875443638} {-0.232329900909} {0.34707278284} {0.185382330479} {-0.0671807083683} {0.110836848354} {0.0152579236072} {-0.0596710301532} {0.374392069385} {0.0616413382144} {0.33392324439}
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+{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {features} {scales} {angles} {gridsize} {patchsize} {cannysigma} {queryreader} {imagereader} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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+{r\_l\_luma\_canny\_1\_5\_pmean2\_g8\_3\_hibin\_1000\_itf\_s4\_a12.2012-09-28T11:52:45.157003.json} {0.19013130747} {0.172716992087} {$\sigma = 1.5$, $N = (4, 12)$, $G = (8, 3)$, HIB} {PMEAN2} {4} {12} {8} {3} {1.5} {LUMA} {CANNY} {HIB} {0.275041258083} {0.0585397680496} {0.387272474645} {0.388135094878} {0.0230746649024} {0.498446596959} {0.2053145285} {0.251054267149} {0.0905819758231} {0.0790146139066} {0.38333038339} {-0.156821955313} {0.343647018894} {0.347107866473} {0.351417689726} {0.224264790701} {-0.0234395994942} {0.0744240936259} {0.236555220679} {0.240423875732} {0.254839310162} {-0.156190415934} {0.384500577476} {0.17077173297} {-0.0771255451879} {0.153936668035} {0.102101744924} {0.0131368203178} {0.390466433003} {0.0140520417076} {0.366196536787}
View
23 thesis/results/l_luma_canny_pmean_1.csv
@@ -1,17 +1,6 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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+{r\_l\_luma\_canny\_1\_5\_pmean\_g8\_3\_hi\_1000\_itf\_s4\_a12.2012-09-25T11:53:09.327659.json} {0.220203568362} {0.168425877376} {$\sigma = 1.5$, $N = (4, 12)$, $G = (8, 3)$, HI} {PMEAN} {4} {12} {8} {3} {1.5} {LUMA} {CANNY} {HI} {0.32006761566} {-0.00257733884789} {0.483294340369} {0.415186544765} {0.125729125562} {0.560289678293} {0.128461993571} {0.2786126201} {0.169193460083} {0.165910618692} {0.38059276111} {-0.124332951727} {0.298119063217} {0.384240799344} {0.301695585518} {0.306594152686} {0.0649483104381} {0.0411741950684} {0.145140456935} {0.246900945644} {0.364254908059} {-0.171513005379} {0.268593489298} {0.109161399621} {0.178370555847} {0.282401328761} {0.122898726332} {0.0402291503823} {0.448188032527} {0.131512200427} {0.362971856879}
+{r\_l\_luma\_canny\_1\_5\_pmean\_g8\_3\_hibin\_1000\_itf\_s4\_a12.2012-09-25T12:01:25.148786.json} {0.211241105846} {0.165341786554} {$\sigma = 1.5$, $N = (4, 12)$, $G = (8, 3)$, HIB} {PMEAN} {4} {12} {8} {3} {1.5} {LUMA} {CANNY} {HIB} {0.357591533051} {-0.0515467769578} {0.429073471206} {0.433097268573} {0.0792737959518} {0.544392229098} {0.162210417531} {0.267503565592} {0.0916626504036} {0.0738740343826} {0.348372585422} {-0.0498287197926} {0.220619444396} {0.399260634103} {0.324697093132} {0.281395379767} {0.0786111302581} {0.0421701086717} {0.202753435458} {0.17377208575} {0.311343951355} {-0.220623362276} {0.210615025916} {0.0939097465037} {0.199853321285} {0.276350239712} {0.311801483467} {0.0613567815132} {0.395854616727} {0.146858290583} {0.352198820446}
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12 thesis/results/l_luma_pmean2_1.csv
@@ -1,6 +1,6 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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-{r\_l\_luma\_pmean2\_g8\_3\_emd\_1000\_itf\_s4\_a12.2012-09-28T12:26:08.648277.json} {0.0714772309786} {0.155932833343} {$N = (4, 12)$, $G = (8, 3)$, EMD} {4} {12} {8} {3} {} {EMD} {0.186737247886} {0.0682556837099} {0.119924649595} {0.328399987661} {-0.0760812293126} {0.142582152914} {-0.0809250057081} {0.11840500096} {0.0} {0.244848613698} {0.204502664898} {-0.220507890394} {0.212769539603} {0.289390563535} {0.18275554496} {0.056774949765} {-0.194969409364} {-0.101608242308} {0.0679923167456} {0.0645174717616} {0.154440666063} {-0.133425358973} {0.127331847955} {0.267353068489} {0.188146829473} {-0.0489697227395} {-0.301941767844} {0.0774203860432} {0.2316619584} {-0.0567017842247} {0.0967134270923}
-{r\_l\_luma\_pmean2\_g8\_3\_hi\_1000\_itf\_s4\_a12.2012-09-28T12:20:10.765652.json} {0.163206216031} {0.18167016018} {$N = (4, 12)$, $G = (8, 3)$, HI} {4} {12} {8} {3} {} {HI} {0.260590262632} {0.183004520201} {0.103750687199} {0.397319850474} {-0.0558094378054} {0.34628337627} {0.165434484522} {0.10791261977} {0.0157808970646} {0.312050057867} {0.342401792002} {-0.0501579905862} {0.238494860779} {0.266111758181} {0.289213708075} {0.295464400246} {0.0383475503563} {0.150480876561} {0.0494147248905} {0.12839625281} {0.394790460422} {-0.180216264476} {0.5085016684} {0.167702990596} {0.305128121541} {0.0543338100191} {-0.270190603002} {0.0665326463443} {0.107646565547} {-0.110029163857} {0.430707213929}
-{r\_l\_luma\_pmean2\_g8\_3\_hibin\_1000\_itf\_s4\_a12.2012-09-28T12:20:26.459249.json} {0.161515988541} {0.171778671694} {$N = (4, 12)$, $G = (8, 3)$, HIB} {4} {12} {8} {3} {} {HIB} {0.228397414932} {0.164987453432} {0.131780898725} {0.403755643278} {-0.0712421686126} {0.379471176121} {0.20791079907} {0.0956740093107} {0.0836919851511} {0.296283936088} {0.336951279603} {-0.0420223662481} {0.16252399308} {0.229665969709} {0.257943216814} {0.281459419037} {0.0384374626728} {0.217068554764} {0.0354063609814} {0.121671426256} {0.355760049376} {-0.121607921128} {0.440063868743} {0.104821891158} {0.312050057867} {0.0854228535187} {-0.256237488709} {0.0131342922073} {0.10307601763} {-0.0695256626594} {0.480221222609}
+{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {features} {scales} {angles} {gridsize} {patchsize} {cannysigma} {queryreader} {imagereader} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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View
12 thesis/results/l_luma_pmean_1.csv
@@ -1,6 +1,6 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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-{r\_l\_luma\_pmean\_g8\_3\_cos\_1000\_itf\_s4\_a12.2012-09-28T15:21:50.373898.json} {0.135410562315} {0.145058885113} {$N = (4, 12)$, $G = (8, 3)$, COS} {4} {12} {8} {3} {} {COS} {0.332904334335} {0.0399231357549} {0.214471895264} {0.378797752055} {0.0480207758475} {0.313972645278} {0.00781303649706} {0.223102656814} {0.0798979686802} {0.202452094134} {0.39485734669} {-0.0881001056223} {0.211615721851} {0.316063192617} {0.189311992121} {0.131329983796} {-0.117051189839} {0.0504857012695} {0.212493052381} {-0.0271147249483} {0.0749354630594} {0.109678880228} {0.148868093487} {0.145337891056} {0.315175362379} {0.15979593736} {-0.112476911} {-0.140523082662} {0.0878552971576} {0.0284605671529} {0.265372668564}
-{r\_l\_luma\_pmean\_g8\_3\_emd\_1000\_itf\_s4\_a12.2012-09-27T16:27:49.539514.json} {0.0657060014164} {0.147289136682} {$N = (4, 12)$, $G = (8, 3)$, EMD} {4} {12} {8} {3} {} {EMD} {0.163556072286} {0.130072151976} {0.25145491044} {0.351581163261} {-0.0657651304228} {0.129736913913} {-0.124598818312} {-0.13642315328} {0.0103094153136} {-0.0025773538284} {0.289390563535} {-0.132304734237} {0.238559786828} {0.212219746592} {0.18790358848} {0.125082699039} {-0.0865096054795} {-0.0347268676242} {-0.0243746041163} {-0.0438718807979} {0.0360361554148} {-0.125162957437} {-0.0167203436709} {0.210797611693} {0.195878890958} {0.0824753225086} {-0.0438718807979} {0.0490329111607} {0.31403065472} {-0.19330153713} {0.088976352925}
-{r\_l\_luma\_pmean\_g8\_3\_hi\_1000\_itf\_s4\_a12.2012-09-26T18:40:17.281576.json} {0.156614017312} {0.157680451916} {$N = (4, 12)$, $G = (8, 3)$, HI} {4} {12} {8} {3} {} {HI} {0.235527785438} {0.117193721087} {0.38501581198} {0.482223759443} {0.126453297204} {0.320513742055} {0.0638485395507} {0.176582576895} {-0.0142027854182} {0.112186829234} {0.250805155064} {-0.0637640079622} {0.411917444023} {0.253474191467} {0.168394347481} {0.0895522576616} {-0.041585453873} {0.194402038373} {0.129347352954} {0.0451912082471} {0.207608037993} {0.0367263328709} {0.239383032398} {-0.0322789501444} {0.269852922946} {0.270622151981} {-0.250327790435} {-0.0353184773752} {0.347151424038} {0.0761785763341} {0.282359465162}
-{r\_l\_luma\_pmean\_g8\_3\_hibin\_1000\_itf\_s4\_a12.2012-09-27T15:19:32.259773.json} {0.150926463602} {0.174092195557} {$N = (4, 12)$, $G = (8, 3)$, HIB} {4} {12} {8} {3} {} {HIB} {0.191035114481} {0.152258571517} {0.446819130926} {0.455778776039} {-0.0915060825064} {0.383718971863} {0.16450782478} {0.103138784318} {0.0452489538609} {0.199204912579} {0.357188984622} {-0.151813889041} {0.267248377009} {0.344646439507} {0.23522169597} {0.117176100946} {-0.0860498362199} {0.0551261332198} {0.0880666886533} {-0.0504857012695} {0.310654412208} {0.0233614585204} {0.167958796512} {0.111618677631} {0.321582090523} {0.325289323262} {-0.221367122704} {-0.0740886589727} {0.263480726427} {0.0194805851935} {0.204220131797}
+{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {features} {scales} {angles} {gridsize} {patchsize} {cannysigma} {queryreader} {imagereader} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
+{r\_l\_luma\_pmean\_g8\_3\_l2\_1000\_itf\_s4\_a12.2012-09-28T15:23:33.940518.json} {0.0286487036372} {0.139130706115} {$N = (4, 12)$, $G = (8, 3)$, $L_2$} {PMEAN} {4} {12} {8} {3} {} {LUMA} {LUMA} {$L_2$} {0.096588231665} {0.130072151976} {0.140556847374} {0.245978029973} {-0.00386853708369} {0.170841678717} {-0.0115607151012} {0.0128701088} {0.00773206148519} {-0.0773206148519} {0.245660433934} {-0.215319469444} {0.00128951236123} {0.201930304333} {0.2445320672} {0.00386853708369} {-0.0167854458393} {0.153055453603} {0.0397690909267} {0.136777040135} {0.149292643861} {-0.109678880228} {-0.0810293577898} {0.102828103265} {-0.0231961844556} {-0.103094153136} {-0.327748756549} {-0.198712324178} {0.10553489216} {-0.103094153136} {-0.0296587843083}
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+{r\_l\_luma\_pmean\_g8\_3\_emd\_1000\_itf\_s4\_a12.2012-09-27T16:27:49.539514.json} {0.0657060014164} {0.147289136682} {$N = (4, 12)$, $G = (8, 3)$, EMD} {PMEAN} {4} {12} {8} {3} {} {LUMA} {LUMA} {EMD} {0.163556072286} {0.130072151976} {0.25145491044} {0.351581163261} {-0.0657651304228} {0.129736913913} {-0.124598818312} {-0.13642315328} {0.0103094153136} {-0.0025773538284} {0.289390563535} {-0.132304734237} {0.238559786828} {0.212219746592} {0.18790358848} {0.125082699039} {-0.0865096054795} {-0.0347268676242} {-0.0243746041163} {-0.0438718807979} {0.0360361554148} {-0.125162957437} {-0.0167203436709} {0.210797611693} {0.195878890958} {0.0824753225086} {-0.0438718807979} {0.0490329111607} {0.31403065472} {-0.19330153713} {0.088976352925}
+{r\_l\_luma\_pmean\_g8\_3\_hi\_1000\_itf\_s4\_a12.2012-09-26T18:40:17.281576.json} {0.156614017312} {0.157680451916} {$N = (4, 12)$, $G = (8, 3)$, HI} {PMEAN} {4} {12} {8} {3} {} {LUMA} {LUMA} {HI} {0.235527785438} {0.117193721087} {0.38501581198} {0.482223759443} {0.126453297204} {0.320513742055} {0.0638485395507} {0.176582576895} {-0.0142027854182} {0.112186829234} {0.250805155064} {-0.0637640079622} {0.411917444023} {0.253474191467} {0.168394347481} {0.0895522576616} {-0.041585453873} {0.194402038373} {0.129347352954} {0.0451912082471} {0.207608037993} {0.0367263328709} {0.239383032398} {-0.0322789501444} {0.269852922946} {0.270622151981} {-0.250327790435} {-0.0353184773752} {0.347151424038} {0.0761785763341} {0.282359465162}
+{r\_l\_luma\_pmean\_g8\_3\_hibin\_1000\_itf\_s4\_a12.2012-09-27T15:19:32.259773.json} {0.150926463602} {0.174092195557} {$N = (4, 12)$, $G = (8, 3)$, HIB} {PMEAN} {4} {12} {8} {3} {} {LUMA} {LUMA} {HIB} {0.191035114481} {0.152258571517} {0.446819130926} {0.455778776039} {-0.0915060825064} {0.383718971863} {0.16450782478} {0.103138784318} {0.0452489538609} {0.199204912579} {0.357188984622} {-0.151813889041} {0.267248377009} {0.344646439507} {0.23522169597} {0.117176100946} {-0.0860498362199} {0.0551261332198} {0.0880666886533} {-0.0504857012695} {0.310654412208} {0.0233614585204} {0.167958796512} {0.111618677631} {0.321582090523} {0.325289323262} {-0.221367122704} {-0.0740886589727} {0.263480726427} {0.0194805851935} {0.204220131797}
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12 thesis/results/l_luma_segment_pmean2_1.csv
@@ -1,6 +1,6 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
-{r\_l\_luma\_segment\_pmean2\_g8\_3\_l2\_1000\_itf\_s4\_a12.2012-09-28T15:40:19.576241.json} {0.090573527192} {0.117669329741} {$N = (4, 12)$, $G = (8, 3)$, $L_2$} {4} {12} {8} {3} {} {$L_2$} {0.225372540552} {-0.00128784308887} {0.210190514881} {0.12492077962} {0.127661723762} {0.224791682523} {0.168272630917} {0.0463323916799} {0.0644338457099} {0.0695885533667} {0.0707399155307} {-0.171217891365} {0.050290982088} {0.122187126826} {0.23423598016} {0.135398797929} {0.0916743580454} {0.057878112707} {0.237331671659} {0.072259568373} {0.0926643996381} {-0.0812914053454} {0.101608242308} {0.185090585877} {-0.0670111995383} {0.0618564918815} {-0.190971716414} {-0.108388540461} {0.22136587136} {0.286086274952} {0.145714896819}
-{r\_l\_luma\_segment\_pmean2\_g8\_3\_cos\_1000\_itf\_s4\_a12.2012-09-28T15:41:28.490648.json} {0.153551493726} {0.130950652901} {$N = (4, 12)$, $G = (8, 3)$, COS} {4} {12} {8} {3} {} {COS} {0.167108805451} {0.00388853618958} {0.346383206671} {0.157419879026} {0.0428293406208} {0.36106854207} {0.286382892005} {0.207524268788} {0.196636645783} {0.271656051265} {0.18770324831} {-0.0808720815511} {0.209326868132} {0.21812315927} {0.304010688488} {0.454401743934} {0.220254907947} {0.0647252580379} {0.325455329488} {-0.0159511578925} {0.0524311244234} {0.156735882789} {0.102720537601} {0.169076224702} {-0.0475434658643} {0.0808972485006} {0.0362695517652} {0.0279154141739} {0.224645066272} {-0.00646831071656} {0.0353408998144}
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-{r\_l\_luma\_segment\_pmean2\_g8\_3\_hi\_1000\_itf\_s4\_a12.2012-09-28T11:32:16.037493.json} {0.150999412585} {0.137503560198} {$N = (4, 12)$, $G = (8, 3)$, HI} {4} {12} {8} {3} {} {HI} {0.156184253415} {-0.00907325110902} {0.292881909942} {0.126452033972} {0.0770251396228} {0.358929721297} {0.276433900627} {0.222907893324} {0.208414283138} {0.239170242038} {0.141101062523} {-0.122493423377} {0.219430190092} {0.198849519696} {0.324415650481} {0.512292583723} {0.274252885379} {0.096294921355} {0.262019121198} {-0.0239267368388} {0.102240692626} {0.0349741226058} {0.100221474595} {0.186126338326} {0.00409857483632} {0.137213591115} {0.0712898301423} {-0.00134030677289} {0.27808247649} {-0.00388098642994} {-0.0596059178816}
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+{r\_l\_luma\_segment\_pmean2\_g8\_3\_l2\_1000\_itf\_s4\_a12.2012-09-28T15:40:19.576241.json} {0.090573527192} {0.117669329741} {$N = (4, 12)$, $G = (8, 3)$, $L_2$} {PMEAN2} {4} {12} {8} {3} {} {LUMA} {SEGMENT} {$L_2$} {0.225372540552} {-0.00128784308887} {0.210190514881} {0.12492077962} {0.127661723762} {0.224791682523} {0.168272630917} {0.0463323916799} {0.0644338457099} {0.0695885533667} {0.0707399155307} {-0.171217891365} {0.050290982088} {0.122187126826} {0.23423598016} {0.135398797929} {0.0916743580454} {0.057878112707} {0.237331671659} {0.072259568373} {0.0926643996381} {-0.0812914053454} {0.101608242308} {0.185090585877} {-0.0670111995383} {0.0618564918815} {-0.190971716414} {-0.108388540461} {0.22136587136} {0.286086274952} {0.145714896819}
+{r\_l\_luma\_segment\_pmean2\_g8\_3\_cos\_1000\_itf\_s4\_a12.2012-09-28T15:41:28.490648.json} {0.153551493726} {0.130950652901} {$N = (4, 12)$, $G = (8, 3)$, COS} {PMEAN2} {4} {12} {8} {3} {} {LUMA} {SEGMENT} {COS} {0.167108805451} {0.00388853618958} {0.346383206671} {0.157419879026} {0.0428293406208} {0.36106854207} {0.286382892005} {0.207524268788} {0.196636645783} {0.271656051265} {0.18770324831} {-0.0808720815511} {0.209326868132} {0.21812315927} {0.304010688488} {0.454401743934} {0.220254907947} {0.0647252580379} {0.325455329488} {-0.0159511578925} {0.0524311244234} {0.156735882789} {0.102720537601} {0.169076224702} {-0.0475434658643} {0.0808972485006} {0.0362695517652} {0.0279154141739} {0.224645066272} {-0.00646831071656} {0.0353408998144}
+{r\_l\_luma\_segment\_pmean2\_g8\_3\_emd\_1000\_itf\_s4\_a12.2012-09-28T11:38:19.684333.json} {0.0681156733771} {0.131257314511} {$N = (4, 12)$, $G = (8, 3)$, EMD} {PMEAN2} {4} {12} {8} {3} {} {LUMA} {SEGMENT} {EMD} {0.289764694995} {-0.212494109663} {0.300456380167} {-0.106890976376} {-0.0116056112511} {0.214515491321} {0.150289296315} {0.32175272} {0.0180414767988} {0.0283508921123} {0.181351419815} {-0.0674494723559} {0.140556847374} {-0.0244374253652} {0.17503347968} {0.135398797929} {0.220793172194} {0.0321545070594} {0.198845454633} {-0.0206455909637} {0.211068910287} {-0.0141937374413} {-0.0141479831061} {0.0514140516324} {-0.134022399077} {-0.0515470765679} {0.0619367728911} {-0.0335488339521} {0.02059217408} {0.0592791380531} {-0.00902658652862}
+{r\_l\_luma\_segment\_pmean2\_g8\_3\_hi\_1000\_itf\_s4\_a12.2012-09-28T11:32:16.037493.json} {0.150999412585} {0.137503560198} {$N = (4, 12)$, $G = (8, 3)$, HI} {PMEAN2} {4} {12} {8} {3} {} {LUMA} {SEGMENT} {HI} {0.156184253415} {-0.00907325110902} {0.292881909942} {0.126452033972} {0.0770251396228} {0.358929721297} {0.276433900627} {0.222907893324} {0.208414283138} {0.239170242038} {0.141101062523} {-0.122493423377} {0.219430190092} {0.198849519696} {0.324415650481} {0.512292583723} {0.274252885379} {0.096294921355} {0.262019121198} {-0.0239267368388} {0.102240692626} {0.0349741226058} {0.100221474595} {0.186126338326} {0.00409857483632} {0.137213591115} {0.0712898301423} {-0.00134030677289} {0.27808247649} {-0.00388098642994} {-0.0596059178816}
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View
12 thesis/results/l_luma_segment_pmean_1.csv
@@ -1,6 +1,6 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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-{r\_l\_luma\_segment\_pmean\_g8\_3\_hibin\_1000\_itf\_s4\_a12.2012-09-28T11:34:04.842584.json} {0.163027562141} {0.131452173937} {$N = (4, 12)$, $G = (8, 3)$, HIB} {4} {12} {8} {3} {} {HIB} {0.314519700241} {0.00654929547713} {0.283393646357} {0.259035468589} {0.0622860787343} {0.426795251121} {0.16748555306} {0.204929951009} {0.276350239712} {0.180740779667} {0.167208460775} {0.104585021327} {0.195538871525} {-0.00133132307396} {0.307131506387} {0.335193290952} {0.19247336834} {0.0799343032371} {0.276346363593} {0.00259068226894} {0.224973217508} {-0.0778715284014} {0.115964887599} {0.110775711285} {-0.0554181583117} {0.0297786032651} {0.407378416214} {-0.0274605092902} {0.181551325798} {0.23092462526} {0.0715013261385}
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View
12 thesis/results/l_luma_sobel_pmean2_1.csv
@@ -1,6 +1,6 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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12 thesis/results/l_luma_sobel_pmean_1.csv
@@ -1,6 +1,6 @@
-{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {scales} {angles} {gridsize} {patchsize} {cannysigma} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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-{r\_l\_luma\_sobel\_pmean\_g8\_3\_hibin\_1000\_itf\_s4\_a12.2012-09-28T14:52:43.103812.json} {0.186833698667} {0.174668901699} {$N = (4, 12)$, $G = (8, 3)$, HIB} {4} {12} {8} {3} {} {HIB} {0.207991927493} {-0.00647249326515} {0.12543246251} {0.498803375818} {-0.0498061453641} {0.502385927389} {0.170636738678} {0.164766297575} {0.156972890746} {0.106040489335} {0.412726052698} {-0.0284703432478} {0.277846203751} {0.412345735869} {0.495787135666} {0.351570311078} {-0.0103494270504} {0.0194682100329} {0.221691255049} {0.0310881872273} {0.38747471875} {0.0236393970833} {0.161253282856} {0.112168833957} {0.258518851151} {0.107792571404} {-0.0104992663074} {-0.0224760391403} {0.322624338286} {-0.011695968159} {0.402589146816}
+{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {features} {scales} {angles} {gridsize} {patchsize} {cannysigma} {queryreader} {imagereader} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
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4 thesis/results/parameter_angles.csv
@@ -0,0 +1,4 @@
+{ConfigFilename} {MeanCorrelation} {StandardDeviation} {Description} {features} {scales} {angles} {gridsize} {patchsize} {cannysigma} {queryreader} {imagereader} {metric} {0.png} {1.png} {10.png} {11.png} {12.png} {13.png} {14.png} {2.png} {20.png} {21.png} {23.png} {24.png} {25.png} {26.png} {27.png} {28.png} {30.png} {33.png} {34.png} {38.png} {4.png} {42.png} {43.png} {45.png} {46.png} {47.png} {48.png} {6.png} {7.png} {8.png} {9.png}
+{r\_l\_luma\_canny\_1\_5\_pmean\_g8\_3\_hi\_1000\_itf\_s4\_a8.2012-09-25T11:18:14.201100.json} {0.207284032182} {0.16809350254} {$\sigma = 1.5$, $N = (4, 8)$, $G = (8, 3)$, HI} {PMEAN} {4} {8} {8} {3} {1.5} {LUMA} {CANNY} {HI} {0.205030769015} {0.0272197533271} {0.51128951975} {0.471537194387} {0.122977578115} {0.441918619499} {0.270442015012} {0.272681024995} {0.0734971428541} {0.0787609009556} {0.508787817063} {-0.159393078929} {0.318286476727} {0.368599673882} {0.313478277762} {0.394058308314} {0.083146387773} {0.135481597119} {0.149032261093} {0.238578441859} {0.248058897961} {-0.11277913411} {0.317980763871} {0.0853733248163} {0.140200203025} {0.185807843727} {0.0157488994611} {-0.00810381020314} {0.345806739485} {0.154895957742} {0.227404631301}
+{r\_l\_luma\_canny\_1\_5\_pmean\_g8\_3\_hi\_1000\_itf\_s4\_a12.2012-09-25T11:53:09.327659.json} {0.220203568362} {0.168425877376} {$\sigma = 1.5$, $N = (4, 12)$, $G = (8, 3)$, HI} {PMEAN} {4} {12} {8} {3} {1.5} {LUMA} {CANNY} {HI} {0.32006761566} {-0.00257733884789} {0.483294340369} {0.415186544765} {0.125729125562} {0.560289678293} {0.128461993571} {0.2786126201} {0.169193460083} {0.165910618692} {0.38059276111} {-0.124332951727} {0.298119063217} {0.384240799344} {0.301695585518} {0.306594152686} {0.0649483104381} {0.0411741950684} {0.145140456935} {0.246900945644} {0.364254908059} {-0.171513005379} {0.268593489298} {0.109161399621} {0.178370555847} {0.282401328761} {0.122898726332} {0.0402291503823} {0.448188032527} {0.131512200427} {0.362971856879}
+{r\_l\_luma\_canny\_1\_5\_pmean\_g8\_3\_hi\_1000\_itf\_s4\_a16.2012-09-25T13:08:15.617247.json} {0.206549046179} {0.168050145482} {$\sigma = 1.5$, $N = (4, 16)$, $G = (8, 3)$, HI} {PMEAN} {4} {16} {8} {3} {1.5} {LUMA} {CANNY} {HI} {0.306197266885} {0.00773201654368} {0.442386524776} {0.395738658797} {0.132124463177} {0.505053619935} {0.279049514484} {0.327910483376} {0.23006497858} {0.0838177459792} {0.467820540969} {-0.129770361551} {0.349275106586} {0.375271229048} {0.365943261488} {0.330536995838} {0.0598054517183} {0.111942867745} {0.189069680389} {0.202927067608} {0.347378079735} {0.00693606041074} {0.171208529825} {0.0213757087397} {0.00401553537234} {0.162581863261} {-0.012987012987} {-0.0721886005244} {0.344385355978} {0.100280958035} {0.297136841321}
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BIN  thesis/thesis.pdf
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