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Learning Discriminative Part Detectors for Image Classification and Cosegmentation

Jian Sun 1, 2 Jean Ponce 2, 3 
2 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique - ENS Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : In this paper, we address the problem of learning discriminative part detectors from image sets with category labels. We propose a novel latent SVM model regularized by group sparsity to learn these part detectors. Starting from a large set of initial parts, the group sparsity regularizer forces the model to jointly select and optimize a set of discriminative part detectors in a max-margin framework. We propose a stochastic version of a proximal algorithm to solve the corresponding optimization problem. We apply the proposed method to image classification and cosegmentation, and quantitative experiments with standard benchmarks show that it matches or improves upon the state of the art.
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Submitted on : Thursday, January 16, 2014 - 7:43:11 PM
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  • HAL Id : hal-00932380, version 1



Jian Sun, Jean Ponce. Learning Discriminative Part Detectors for Image Classification and Cosegmentation. ICCV 2013 - International conference on computer vision, Dec 2013, Sydney, Australia. ⟨hal-00932380⟩



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