Local Feature Saliency for Texture Representation

Towards the goal of object/region recognition in images, texture characterization is a very important and challenging task. In this study, we propose a salient point based texture representation scheme. It is a two-phase analysis in the multiresolution framework of discrete wavelet transform. In the first phase, each wavelet sub-band (LH or HL or HH) is used to compute multiple texture features, which represents various aspects of texture. These features are converted into binary images, called salient point images (SPIs), via an automatic threshold technique that maximizes inter-block pattern deviation (IBPD) metric. Such operation may facilitate combining multiple features for better segmentation. In the final phase, we have proposed a set of new texture features, namely non-salient point density (NSPD), salient point residual (SPR), saliency and non-saliency product (SNP). These features characterize various aspects of image texture like fineness/coarseness, primitive distribution, internal structures etc. K-means algorithm is used to cluster the generated features for unsupervised segmentation. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness of the proposed features compared to the wavelet energy (WE) and local extrema density feature (LED).