A Maximum Entropy Framework for Part-Based Texture and Object Recognition

Svetlana Lazebnik 1 Cordelia Schmid 2, * Jean Ponce 1
* Auteur correspondant
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of scale- or affine- invariant keypoints. Object classes are represented using a dictionary of composite semi-local parts, or groups of neighboring keypoints with stable and distinctive appearance and geometric layout. A discriminative maximum entropy framework is used to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set. Experiments on two texture and two object databases demonstrate the effectiveness of this framework for visual classification.
Type de document :
Communication dans un congrès
10th International Conference on Computer Vision (ICCV '05), Oct 2005, Beijing, China. IEEE Computer Society, 1, pp.832 - 838, 2005, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1541339〉. 〈10.1109/ICCV.2005.10〉
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Svetlana Lazebnik, Cordelia Schmid, Jean Ponce. A Maximum Entropy Framework for Part-Based Texture and Object Recognition. 10th International Conference on Computer Vision (ICCV '05), Oct 2005, Beijing, China. IEEE Computer Society, 1, pp.832 - 838, 2005, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1541339〉. 〈10.1109/ICCV.2005.10〉. 〈inria-00548510〉

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