DMCA
Learning Discriminative Part Detectors for Image Classification and
| Citations: | 13 - 1 self |
BibTeX
@MISC{Sun_learningdiscriminative,
author = {Jian Sun and École Normale Supérieure},
title = {Learning Discriminative Part Detectors for Image Classification and},
year = {}
}
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Abstract
This is a preliminary version accepted for publication to ICCV 2013 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. 1.
Keyphrases
discriminative part detector image classification novel latent svm model stochastic version quantitative experiment part detector proximal algorithm initial part large set standard benchmark preliminary version group sparsity image set corresponding optimization problem category label max-margin framework group sparsity regularizer
