Video segmentation scheme based on AMC

Abstract

Video segmentation has become a fundamental of various multimedia applications. Spatiotemporal coherence is important for video segmentation. In this study, to balance the spatiotemporal coherence in scenes with deformation or large motion, the authors propose a novel segmentation scheme based on the absorbing Markov chain (AMC) model named directed graph segmentation based on AMC. In their study, they first generate primary proposals per frame. Then, they train weight models by using a part of primary proposals with their features and feature scores. Next, they construct a directed AMC graph, in which states are the generated primary proposals and edge weights are decided by trained weight models. They subsequently perform the first proposal selection per frame by thresholding the modified absorbed time. Afterwards, they design a reselection algorithm to filter the selected proposals and ensure the proposals, which are the most likely to be the motion object in each frame, to be selected as candidates. Finally, they employ the graph-cuts based optimisation algorithm to generate refined per pixel segmentation by using object and background models built by candidate proposals under the concept of Gaussian mixture models. Experimental results demonstrate that the proposed scheme shows competitive performance compared with advanced algorithms.