Video object segmentation with shape cue based on spatiotemporal superpixel neighbourhood

Abstract

In this study, the authors present a method to extract moving objects in image sequences. The proposed approach is based on a graph cuts algorithm defined on a spatiotemporal superpixel neighbourhood. Presegmented superpixels are partitioned into foreground and background while preserving temporal and spatial coherence. It achieves this goal by three steps. First, instead of operating at pixel level, the superpixels are advocated as basic units of the authors segmentation scheme. Second, within the graph cuts framework, two superpixel‐based data terms and two superpixel‐based smoothness terms are proposed to solve segmentation problem. Finally, the proposed method yields the segmentation of all the superpixels within video volume by the graph cuts algorithm. To illustrate the advantages of this approach, the quantitative and qualitative results are compared with other state‐of‐the‐art methods. The experimental results show that the proposed method gives better performance of segmentation with respect to these methods.

Keywords

• superpixel based smoothness
• superpixel based data
• pixel level operation
• image sequence
• moving object extraction
• spatiotemporal superpixel neighbourhood