This python package implements Spatial Pyramid Matching [1] to recognize images under different categories. Furthermore, Earth Mover's distance [2] is also incorporated to better align parts of images when calculating image-to-image distance.
For better explanation, please refer to my report.
- python 2.7.x
- sklearn
- vlfeat (extract SIFT features)
- numpy
- scipy
In terms of vlfeat, do the following steps to ensure SIFT extractions succeed.
- Download vlfeat and remember the path of vlfeat of your environment
- Update the following path at line 84 in
/recognition/utils.pywith your own path of vlfeat
os.environ['PATH'] += os.pathsep +'/Users/GongLi/Dropbox/FYP/PythonProject/vlfeat/bin/maci64'
os.system(cmmd)Below are two examples of two approaches to recognise images.
python SpatialPyramidExample.py
python EarthMoverDistanceExample.py
The image data set, in which there are six classes (shooting, playing guitar, running, phoning, riding bike and riding horse) as shown in Figure 13, comes from [3]. For each class, there are 60 images with different backgrounds, different persons and different viewpoints.
Recognition accuracies (percent) of different pyramids using SVMs with different kernels
Comparison of recognition accuracy (percent) between aligned distance and unaligned distance
[1] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, vol. 2. IEEE, 2006, pp. 2169–2178.
[2] Y. Rubner, C. Tomasi, and L. J. Guibas, “The earth mover’s distance as a metric for image retrieval,” International Journal of Computer Vision, vol. 40, no. 2, pp. 99–121, 2000.
[3] P. Li, J. Ma, and S. Gao, “Actions in still web images: Visualization, detec- tion and retrieval,” in Web-Age Information Management. Springer, 2011, pp. 302–313.
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