A Statistical Approach to Texture Classification from Single Images
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Shape from Texture without Boundaries
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This paper presents a framework for texture recognitionbased on local affine-invariant descriptors and their spatiallayout. At modeling time, a generative model of localdescriptors is learned from sample images using the EM algorithm.The EM framework allows the incorporation ofunsegmented multi-texture images into the training set. Thesecond modeling step consists of gathering co-occurrencestatistics of neighboring descriptors. At recognition time,initial probabilities computed from the generative modelare refined using a relaxation step that incorporates co-occurrencestatistics. Performance is evaluated on imagesof an indoor scene and pictures of wild animals.