Contourlet-based feature extraction for object recognition

Hong Pan; Xiao-Bin Li; Li-Zuo Jin; Si-Yu Xia

doi:10.1117/12.833077

30 October 2009 Contourlet-based feature extraction for object recognition

Hong Pan, Xiao-Bin Li, Li-Zuo Jin, Si-Yu Xia

Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 749522 (2009) https://doi.org/10.1117/12.833077
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Hong Pan, Xiao-Bin Li, Li-Zuo Jin, and Si-Yu Xia "Contourlet-based feature extraction for object recognition", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 749522 (30 October 2009); https://doi.org/10.1117/12.833077

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