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. Astronishingly, such an approach outperforms the texture classification of finely-designed, state-of-the-art texture filters.
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Journal articles
Liu, L., Y. Long, P. Fieguth, S. Lao, and G. Zhao, "BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification
", IEEE Transactions on Image Processing, vol. 23, no. 7, 2014. Details
Liu, L., P. Fieguth, D. A. Clausi, and G. Kuang, "Sorted Random Projections for Robust Rotation Invariant Texture Classification", Pattern Recognition, vol. 45, no. 6, pp. 2405-2418, June, 2012. Details
Liu, L., and P. Fieguth, "Texture classification from random features", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, issue 3, pp. 574 - 586, 2012. Details
Liu, L., P. Fieguth, L. Zhao, Y. Long, and G. Kuang, "Extended Local Binary Patterns for Texture Classification", Image and Vision Computing, vol. 30, no. 2, pp. 86-99, 2012. Details
Jobanputra, R., and D. A. Clausi, "Preserving boundaries for image texture segmentation using grey level co-occurring probabilities", Pattern Recognition, vol. 39, no. 2, pp. 234 - 245, 2006. Details
Conference papers
Liu, L., P. Fieguth, and G. Kuang, "Generalized Local Binary Patterns for Texture Classification", British Machine Vision Conference, Dundee, 2011. Details
Liu, L., P. Fieguth, G. Kuang, and H. Zha, "Sorted Random Projections for Robust Texture Classification",International Conference on Computer Vision (ICCV), Barcelona, 2011. Details
Liu, L., P. Fieguth, and G. Kuang, "Combining Sorted Random Features for Texture Classification", International Conference on Image Processing, Brussels, 2011. Details
Liu, L., P. Fieguth, and G. Kuang, "Compressed sensing for robust texture classification", 10th Asian Conference on Computer Vision (ACCV'10), pp. 383–396, 2010. Details
Clausi, D. A., and B. Yue, "Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields", 17th International Conference on Pattern Recognition (ICPR), vol. 1, Cambridge, United Kingdom, pp. 584 - 587, Aug. 23 - 26, 2004. Details
Clausi, D. A., and H. Deng, "Feature fusion for image texture segmentation", 17th International Conference on Pattern Recognition (ICPR), vol. 1, pp. 580 - 583, 2004. Details
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. Details
Thesis
Jobanputra, R., "Preserving Texture Boundaries for SAR Sea Ice Segmentation
", Department of Systems Design Engineering, Waterloo, Ontario, Canada, University of Waterloo, 2004. Details
Clausi, D. A., "Texture Segmentation of SAR Sea Ice Imagery", Department of Systems Design Engineering, Waterloo, Ontario, Canada, University of Waterloo, pp. 176, 1996. Details
