Test Code used for the paper: "On the Influence of Superpixel Methods for Image Parsing", Strassburg et al., Proc. International Conference on Computer Vision Theory and Applications (Visapp), 2015
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Edge_Avoiding_Wavelets
Saliency/region_contrast_saliency-master
SuperParsing/im_parser
segparsing
README.txt

README.txt

===================================================================================
Copyright Information
===================================================================================

InfluenceSegImageParsing Code
Created by Johann Straßburg (johann.strassburg@udo.edu) 2014

This code implements the test code used for the following paper:

"On the Influence of Superpixel Methods for Image Parsing"
Johann Strassburg, Rene Grzeszick, Leonard Rothacker, Gernot A. Fink
Proc. International Conference on Computer Vision Theory and Applications (Visapp), 2015.
http://patrec.cs.tu-dortmund.de/cms/en/home/Publications/index.php

It was originally used in the master thesis:
"Segmentierungsverfahren für Bildparsing in natürlichen Szenen", Johann Strassburg (2014)
http://patrec.cs.tu-dortmund.de/cms/en/home/Publications/Theses/index.html  

===================================================================================
This code is based on previous work and code by (and not limited to):

Folder: "SuperParsing":
Joseph Tighe and Svetlana Lazebnik, "SuperParsing: Scalable Nonparametric 
Image Parsing with Superpixels," European Conference on Computer Vision, 2010.

Folder: "Edge_Avoiding_Wavelets":
Fattal,  R.  (2009). "Edge-avoiding  wavelets  and  their  ap-
plications." ACM  Transactions  on  Graphics  (TOG)

Folder: "Saliency":
B. Schauerte, R. Stiefelhagen, "How the Distribution of Salient 
Objects in Images Influences Salient Object Detection". In 
Proceedings of the 20th International Conference on Image Processing
(ICIP), 2013.

For more information relate to code-documentation within the program-structure 
and/or notes within the thesis and/or the paper(s)

===================================================================================

Instructions to perform SuperParsing with different superpixels methods

===================================================================================

Contents:
** 1. Experiment File Structure
** 2. SuperParsing
** 3. Superpixel Creation
** 4. Further Tools

Folder Overview:
*Edge_Avoiding_Wavelets
*Saliency
*segparsing
*SuperParsing

========================================
	1. Experiment File Structure
========================================

This section gives an overview how an Experiment can be structured for SuperParsing evaluation as follows:
****************************************************
+parent folder+
	+subfolder 1+
	+subfolder 2+
		+subsubfolder+ #folder description
	<file>
	*folder/file provided by database* #(other folders/files are/needs to be created by e.g. superParsing algorithm (see section 2)) 
****************************************************


+Database+ 	#for example 'Barcelona' for 'Barcelona' dataset
	+Images+	#folder containing Images of the dataset
	+Experiments+	#folder containing different superParsing experiments (evaluation, superpixel configurations etc.)
		+experiment_1+ #one experiment e.g. 'SLIC_50_1_1' for an experiment using SLIC superpixels
		+experiment_2+
			+*GeoLabels*+		#folder containing geometric labels to images
				+SP_Desc_k200+		#folder containing geometric labels to superpixels 
			+*SemanticLabels*+	#folder containing semantic labels to images
				+SP_Desc_k200+		#folder containing semantic labels to superpixels
			<*TestSet1.txt*> #file linking to the test set of images (Attention: check correct addressing for Linux/Windows computers)
			+Python+	#folder for Python output created by superParsing.py in segparsing project
			+Data+ #contains most of produced output by SuperParsing
				+Base+
					+GeoLabels+		#contains processed geometric label information of testset
					+SemanticLabels+	#contains processed semantic label information of testset
					+MRF+			#contains raw SuperParsing results for testset
					+RetrievalSet		#contains retrieval sets for each image of testset
					<ResultsMRF>		#Summarized results of SuperParsing
				+Descriptors+	
					+Global+	#contains global descriptors of all Images
							#can be created once for one database and copied to new experiments
					
					+SP_Desc_k200+	#contains all superpixel descriptors as well as superpixels
							#name originally from from Graph-Based configuration within original SuperParsing configuration
							#remains the same if parameters are not changed (also if other superpixels are used, see section 2)
						+super_pixels	#folder contains superpixels
							



========================================
	2. SuperParsing
========================================
To perform SuperParsing go to SuperParsing/im_parser:

++ 1.Execution ++
Files 'RunSift*.m', 'RunBarca*.m' can be run to perform SuperParsing on the SiftFlow/Barcelona Dataset.
Input argument is the experiment folder name e.g. 'Quick_Shift_10_48_0.05'

++ 2.Options ++
Options can be found in SuperParsing/im_parser/DataSpecific:
Files 'RunSiftFlow*.m', 'RunBarcelona*.m', called by executed file in 2.1 inherits options as well as Data paths

++ 3. Example execution ++
- 1 -
**** Perform SuperParsing on a Quick Shift experiment with precalculated ****
**** superpixels on the Barcelona Dataset                                **** 
nohup nice /vol/local/amd64/matlab2013b/bin/matlab -nodisplay -nodesktop -r "RunBarca('Quick_Shift_10_48_0.05'); exit;"

- 2 -
**** Perform SuperParsing on an experiment with Ground Truth superpixels  ****
**** superpixels on the Barcelona Dataset                                ****
nohup nice /vol/local/amd64/matlab2013b/bin/matlab -nodisplay -nodesktop -r "RunGTBarca('GroundTruth'); exit;" 

- 3 -
**** Perform SuperParsing using and creating superpixels by the          ****
**** Graph-Based approach on the SiftFlow dataset                        ****
nohup nice /vol/local/amd64/matlab2013b/bin/matlab -nodisplay -nodesktop -r "RunSift('Graph_based'); exit;"


========================================
	3. Superpixel Creation
========================================

++ 1. Groundtruth superpixels ++

Use SuperParsing algorithm (section 2) to create Groundtruth segments and perform SuperParsing.
See section 2.3.2 for an example execution on the Barcelona dataset

++ 2. Efficient Graph-Based image segmentation ++

Use SuperParsing algorithm (section 2) to create Graph-Based segments and perform SuperParsing.
See section 2.3.3 for an example execution on the SiftFlow dataset

++ 3. SLIC ++

Go to segparsing folder and execute superParsing.py in 'main'-folder (you may need to copy file into parent folder, if using it not as a project import (e.g. in eclipse))
See usage of superParsing script for parameters input.
Change global path variables in segparsing/utils/utils.py.

++ 4. Quick Shift ++

Go to segparsing folder and execute superParsing.py in 'main'-folder (you may need to copy file into parent folder, if using it not as a project import (e.g. in eclipse))
See usage of superParsing script for parameters input.
Change global path variables in segparsing/utils/utils.py.

++ 5. Grid based segmentation ++

Go to segparsing folder and execute superParsing.py in 'main'-folder (you may need to copy file into parent folder, if using it not as a project import (e.g. in eclipse))
See usage of superParsing script for parameters input.
Change global path variables in segparsing/utils/utils.py.

++ 6. Saliency ++

Go to Saliency/region_contrast_saliency-master folder.
Copy images to process into a subfolder 'Images' (or change path in segment_images.m).
Create salience superpixels by:

1. Use segment_images.m to create saliency matrices in folder 'segments' from images in folder 'Images'.

2. Use heatsal.py to convert saliency matrices from folder 'segments' into heatmap-images in folder output.

3. Use images from output folder to create superpixel (e.g. Graph-Based segmentation through implementation within SuperParsing (see section 3.2.).

4. Use created superpixels in SuperParsing code (Attention: if created by SuperParsing code, do not forget to change input images back to normal (not heatmap images) to create segment-features etc.).



++ 7. Edge Avoiding Wavelets ++

- 1. Creating Edge Avoiding Wavelets superpixels based on argmax of scaling functions -

Go to Edge_Avoiding_Wavelets/eaw_code folder.
Copy images to process into a subfolder 'Images' (or change path in eaw_superpixels.m).
Use eaw_superpixels script to create superpixels for images (see eaw_superpixels.m for output information).
ATTENTION: selection of many scales and/or input of big images and/or many images can results in a huge amount of files.
TO SHRINK AMOUNT OF FILES: deactivate creation of images and/or shrink scaling functions to one file per scale (see section 4.1.1).

- 2. Relabel EAW-superpixel to merge smaller regions to next bigger regions in order to have only fully connected areas -

Go to Edge_Avoiding_Wavelets/eaw_relabeling folder.
Use relabel.py to relabel created EAW superpixels.


========================================
	4. Further Tools
========================================

++ 1. Weighting EAW superpixels results by scale functions ++

The results given by EAW superpixels can be improved by weighting superpixels with scaling function weights.
The following steps are needed:

- 1 -
Go to Edge_Avoiding_Wavelets/concat_eaw.
Use eaw_concat.py to concatenate created scaling functions to one file according to superpixel indices (see ReadMe for details).

- 2 -
Go to Edge_Avoiding_Wavelets/eval_eaw_*database*.
Use e.g. eaw_ev.py to evaluate results by weighting superpixel (label selection).
For further details see README

++ 2. Utils ++

Go to segparsing/utils to get access to some tools.

- 1. Statistics -

Use Statistics.py to analyze superpixels (e.g. size etc.) of multiple experiments.

- 2. Results -

Use evalcopy.py to copy Results from multiple Experiments to one place.

- 3. Utils -

Use utils.py for different processing tools (e.g. reading of mat-files).