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ICCV
2005
IEEE

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

14 years 5 months ago
A Maximum Entropy Framework for Part-Based Texture and Object Recognition
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.
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2005
Where ICCV
Authors Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
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