Self-explanatory sparse representation for image classification

Bao Di Liu, Yu Xiong Wang, Bin Shen, Yu Jin Zhang, Martial Hebert

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Traditional sparse representation algorithms usually operate in a single Euclidean space. This paper leverages a self-explanatory reformulation of sparse representation, i.e., linking the learned dictionary atoms with the original feature spaces explicitly, to extend simultaneous dictionary learning and sparse coding into reproducing kernel Hilbert spaces (RKHS). The resulting single-view self-explanatory sparse representation (SSSR) is applicable to an arbitrary kernel space and has the nice property that the derivatives with respect to parameters of the coding are independent of the chosen kernel. With SSSR, multiple-view self-explanatory sparse representation (MSSR) is proposed to capture and combine various salient regions and structures from different kernel spaces. This is equivalent to learning a nonlinear structured dictionary, whose complexity is reduced by learning a set of smaller dictionary blocks via SSSR. SSSR and MSSR are then incorporated into a spatial pyramid matching framework and developed for image classification. Extensive experimental results on four benchmark datasets, including UIUC-Sports, Scene 15, Caltech-101, and Caltech-256, demonstrate the effectiveness of our proposed algorithm.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
Number of pages17
EditionPART 2
ISBN (Print)9783319106045
StatePublished - 2014
Externally publishedYes
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: Sep 6 2014Sep 12 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8690 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th European Conference on Computer Vision, ECCV 2014


  • Image Classification
  • Multiple View
  • Reproducing Kernel Hilbert Spaces
  • Sparse Representation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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