Texture Classification is the problem of distringuishing between textures, a classic problem in pattern recognition. Since many very sophisticated classifiers exist, the key challenge here is the development of effective features to extract from a given textured image.
Many approaches have been defined to extract features, such as Gabor filters or wavelets, however a great deal of recent work has focused on patch-based methods, whereby a texture is classified strictly based on a set of small patches of pixels extracted from a given textured image.
The difficulty is that too small a patch fails to be sensitive to non-local features of the texture, whereas a very large patch leads to enormous feature vectors and computational problems associated with very high-dimensional pattern recognition.
Recent work by Liu proposed using random linear functions of patches. The number of such random features needed turns out to be relatively modest, therefore it is suddenly feasible to do texture classification using large image patches, then with some number of random features. Astonishingly, such an approach outperforms the texture classification of finely-designed, state-of-the-art texture filters.
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Liu L., Long Y., Fieguth P.W., Lao S., and Zhao G, "BRINT: Binary rotation invariant and noise tolerant texture classification", IEEE Transactions on Image Processing, vol. 23, pp. 3071-3084, 2015. Get it here. Liu L., and Fieguth P, "Texture classification from random features", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, pp. 574-586, 2015. Get it here. Liu L., Zhao L., Long Y., Kuang G., and Fieguth P, "Extended local binary patterns for texture classification", Image and Vision Computing, vol. 30, pp. 86-99, 2015. Get it here. Liu L., Fieguth P., Clausi D., and Kuang G, "Sorted random projections for robust rotation-invariant texture classification", Pattern Recognition, vol. 45, pp. 2405-2418, 2012. Get it here. Jobanputra R., and Clausi D.A, "Preserving boundaries for image texture segmentation using grey level co-occurring probabilities", Pattern Recognition, vol. 39, pp. 234-245, 2006. Get it here.
Conference PapersLiu L., Fieguth P., Kuang G., and Zha H, "Sorted random projections for robust texture classification", Proceedings of the IEEE International Conference on Computer Vision, 2015. Get it here. Liu L., Fieguth P., and Kuang G, "Combining sorted random features for texture classification", Proceedings - International Conference on Image Processing, ICIP, 2015. Get it here. Liu L., Fieguth P., and Kuang G, "Compressed sensing for robust texture classification", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015. Get it here. Liu L., Fieguth P., and Kuang G, "Generalized local binary patterns for texture classification", BMVC 2011 - Proceedings of the British Machine Vision Conference 2011, 2015. Get it here. Clausi D.A., and Deng H, "Feature fusion for image texture segmentation", Proceedings - International Conference on Pattern Recognition, 2004. Get it here. Clausi D.A., and Yue B, "Texture segmentation comparison using grey level Co-occurrence probabilities and Markov random fields", Proceedings - International Conference on Pattern Recognition, 2004. Get it here.
Clausi, D. A., “Towards a Novel Approach for Texture Segmentation of SAR Sea Ice Imagery”, 26th International Symposium on Remote Sensing of Environment and 18th Annual Symposium of the Canadian Remote Sensing Society, Vancouver, BC, Canada, pp. 257-261, 1996. Get it here.
Theses
Jobanputra, R., “Preserving Texture Boundaries for SAR Sea Ice Segmentation”, Department of Systems Design Engineering, Waterloo, Ontario, Canada, University of Waterloo, 2004. Get it here.
Clausi, D. A., “Texture Segmentation of SAR Sea Ice Imagery”, Department of Systems Design Engineering, Waterloo, Ontario, Canada, University of Waterloo, pp. 176, 1996. Get it here.