Image segmentation via mean shift and loopy belief propagation

This paper presents a novel approach that can quickly and effectively partition images based on fully exploiting the spatially coherent property. We propose an algorithm named iterative loopy belief propagation (iLBP) to integrate the homogenous regions and prove its convergence. The image is first segmented by mean shift (MS) algorithm to form over-segmented regions that preserve the desirable edges and spatially coherent parts. The segmented regions are then represented by region adjacent graph (RAG). Motivated by k-means algorithm, the iLBP algorithm is applied to perform the minimization of the cost function to integrate the over-segmented parts to get the final segmentation result. The image clustering based on the segmented regions instead of the image pixels reduces the number of basic image entities and enhances the image segmentation quality. Comparing the segmentation result with some existing algorithms, the proposed algorithm shows a better performance based on the evaluation criteria of entropy especially on complex scene images.