Unsupervised Video Object Foreground Segmentation and Co-Localization by Combining Motion Boundaries and Actual Frame Edges

Abstract

In this article the authors proposed a fast and fully unsupervised approach for a foreground object co-localization and segmentation of unconstrained videos. This article first computes both the actual edges and motion boundaries of the video frames, and then aligns them by the proposed HOG affinity map approach. Then, by filling the occlusions generated by the aligned edges, the paper obtained more precise masks about the foreground object. With an accumulation process, these masks could be derived as the motion-based likelihood, which is used as a unary term in the proposed graph model. Another unary term is called color-based likelihood, which is computed by the color distribution of foreground and background. Experiment results shows the method is fast and effective to detect and segment foreground objects.