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Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition

Svetlana Lazebnik 1 Cordelia Schmid 2 Jean Ponce 1 
2 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : We present a framework for texture recognition based on local affine-invariant descriptors and their spatial layout. At modelling time, a generative model of local descriptors is learned from sample images using the EM algorithm. The EM framework allows the incorporation of unsegmented multitexture images into the training set. The second modelling step consists of gathering co-occurrence statistics of neighboring descriptors. At recognition time, initial probabilities computed from the generative model are refined using a relaxation step that incorporates co-occurrence statistics. Performance is evaluated on images of an indoor scene and pictures of wild animals.
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Svetlana Lazebnik, Cordelia Schmid, Jean Ponce. Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition. 9th IEEE International Conference on Computer Vision (ICCV '03), Oct 2003, nice, France. pp.649--655, ⟨10.1109/ICCV.2003.1238409⟩. ⟨inria-00548231⟩



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