Structured sparse coding for image representation based on L 1-graph

Weihua Ou*, Xinge You, Yiu Ming CHEUNG, Qinmu Peng, Mingming Gong, Xiubao Jiang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Sparse coding seeks for over-complete bases to obtain the high-level image representation for image analysis. In many applications, the image data might reside on a low dimensional manifold embedded in high dimensional ambient space. However, standard sparse coding cannot exploit the manifold structure. In this paper, we propose a novel structured sparse coding method based on the L 1-graph, in which the geometric structure of the image data is considered explicitly. Specifically, a new regularization term based on L 1-graph is incorporated into the standard sparse coding framework and a fast iterative thresholding algorithm is developed to solve the optimization problem. Through this coding scheme, the codes obtained by our algorithm between the similar data points in high dimensional space are more similar than that obtained by standard sparse coding. Experiments demonstrate the the efficacy of the proposed method for image representation on two benchmark databases.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages3220-3223
Number of pages4
Publication statusPublished - 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 11 Nov 201215 Nov 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
Country/TerritoryJapan
CityTsukuba
Period11/11/1215/11/12

Scopus Subject Areas

  • Computer Vision and Pattern Recognition

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