Learning a discriminative dictionary with CNN for image classification

Shuai Yu, Tao Zhang, Chao Ma, Lei Zhou, Jie Yang, Xiangjian He

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


In this paper, we propose a novel framework for image recognition based on an extended sparse model. First, inspired by the impressive results of CNN over different tasks in computer vision, we use the CNN models pre-trained on large datasets to generate features. Then we propose an extended sparse model which learns a dictionary from the CNN features by incorporating the reconstruction residual term and the coefficients adjustment term. Minimizing the reconstruction residual term guarantees that the class-specific sub-dictionary has good representation power for the samples from the corresponding class and minimizing the coefficients adjustment term encourages samples from different classes to be reconstructed by different class-specific sub-dictionaries. With this learned dictionary, not only the representation residual but also the representation coefficients will be discriminative. Finally, a metric involving these discriminative information is introduced for image classification. Experiments on Caltech101 and PASCAL VOC 2012 datasets show the effectiveness of the proposed method on image classification.

Original languageEnglish
Title of host publicationNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
EditorsSeiichi Ozawa, Kazushi Ikeda, Derong Liu, Akira Hirose, Kenji Doya, Minho Lee
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319466712
Publication statusPublished - 2016
Externally publishedYes
Event23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
Duration: 16 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9948 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Neural Information Processing, ICONIP 2016


  • Convolutional Neural Networks
  • Image classification
  • Sparse model
  • Unsupervised dictionary learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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