A New Probabilistic Representation of Color Image Pixels and Its Applications

Abstract

This paper proposes a novel probabilistic representation of color image (PRCI) pixels and investigates its applications to similarity construction in motion estimation and image segmentation problems. The PRCI explores the mixture representation of the input image(s) as prior information and describes a given color pixel in terms of its membership in the mixture. Such representation greatly simplifies the estimation of the probability density function from limited observations and allows us to derive a new probabilistic pixel-wise similarity measure based on the continuous domain Bhattacharyya coefficient. This yields a convenient expression of the similarity measure in terms of the pixel memberships. Furthermore, this pixel-wise similarity is extended to measure the similarity between two image regions. The usefulness of the proposed pixel/region-wise similarities is demonstrated by incorporating them, respectively, in a dense image descriptor-based multi-layered motion estimation problem and an unsupervised image segmentation problem. Experimental results show that: 1) the integration of the proposed pixel-wise similarity in dense image-descriptor construction yields improved peak signal to noise ratio performance and higher tracking accuracy in the multi-layered motion estimation problem and 2) the proposed similarity measures give the best performance in terms of all quantitative measurements in the unsupervised superpixel-based image segmentation of the MSRC and BSD300 datasets.