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International Journal of Computer Vision

, Volume 120, Issue 2, pp 111–133 | Cite as

Learning Dictionary of Discriminative Part Detectors for Image Categorization and Cosegmentation

  • Jian Sun
  • Jean Ponce
Article

Abstract

This paper proposes a novel approach to learning mid-level image models for image categorization and cosegmentation. We represent each image class by a dictionary of part detectors that best discriminate that class from the background. We learn category-specific part detectors in a weakly supervised setting in which the training images are only annotated with category labels without part/object location information. We use a latent SVM model regularized using the \(\ell _{2,1}\) group sparsity norm to learn the 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 learned part detectors to image classification and cosegmentation, and present extensive comparative experiments with standard benchmarks.

Keywords

Discriminative parts Discriminative learning Image classification Image cosegmentation 

Notes

Acknowledgments

Jian Sun was supported by NSFC (No. 61472313, 11131006), the 973 program (2013CB329404), NCET-12-0442, and NSFC (No. 61303121). Jean Ponce’s work was supported in part by European Research Council (VideoWorld project) and the Institut Universitaire de France.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Xi’an Jiaotong UniversityXi’anPeople’s Republic of China
  2. 2.École Normale Supérieure / PSL Research UniversityParisFrance

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