Contourlet-based feature extraction for object recognition

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. © 2009 Copyright SPIE - The International Society for Optical Engineering.