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Proceedings Paper

Contourlet-based feature extraction for object recognition
Author(s): Hong Pan; Xiao-Bin Li; Li-Zuo Jin; Si-Yu Xia
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Paper Abstract

A novel contourlet-based local feature descriptor, called Local Contourlet Binary Pattern (LCBP), is developed in this paper. LCBP provides a multiscale and multidirectional representation for images since it integrates contourlet transform with local binary pattern operators. Allowing for the characteristics of marginal and conditional distributions of LCBP as well as simplicity of the model itself, we model LCBP coefficients using a two-state HMT that is in accordance with the intra-band, inter-band and inter-direction distributions of LCBP coefficients. Based on the LCBP-HMT model, we further propose an object recognition method that extracts parameters of the LCBP-HMT model as features and classifies the query sample by comparing the Kullback-Liebler distance between features of the query sample and that of the prototype objects. Experimental results illustrate the superiority of the LCBP over traditional wavelet features and raw statistical features of contourlet coefficients in terms of the discrimination performance.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 749522 (30 October 2009); doi: 10.1117/12.833077
Show Author Affiliations
Hong Pan, Southeast Univ. (China)
Xiao-Bin Li, Southeast Univ. (China)
Li-Zuo Jin, Southeast Univ. (China)
Si-Yu Xia, Southeast Univ. (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Bruce Hirsch; Zhiguo Cao; Hanqing Lu, Editor(s)

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