Video Object Segmentation Using Convex Optimization of Foreground and Background Distributions

Abstract

In this study, a video object segmentation approach using convex optimization of foreground and background distributions is proposed. The proposed approach consists of four stages. First, optical flow computation and superpixel segmentation are performed on video frames. Second, convex optimization with a mixed energy function is employed to estimate the initial foreground and background distributions of video frames. Third, binary label maps for video frames are generated by maximum a posteriori (MAP) estimation. Fourth, the binary label maps are refined to obtain the final video object segmentation maps. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of three comparison approaches.