期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 40, 期 1, 页码 20-33出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2017.2662005
关键词
Video saliency; video object segmentation; geodesic distance; spatiotemporal object prior
资金
- National Basic Research Program of China (973 Program) [2013CB328805]
- National Natural Science Foundation of China [61272359]
- Australian Research Council's Discovery Projects funding scheme [DP150104645]
- Fok Ying-Tong Education Foundation for Young Teachers
Video saliency, aiming for estimation of a single dominant object in a sequence, offers strong object-level cues for unsupervised video object segmentation. In this paper, we present a geodesic distance based technique that provides reliable and temporally consistent saliency measurement of superpixels as a prior for pixel-wise labeling. Using undirected intra-frame and inter-frame graphs constructed from spatiotemporal edges or appearance and motion, and a skeleton abstraction step to further enhance saliency estimates, our method formulates the pixel-wise segmentation task as an energy minimization problem on a function that consists of unary terms of global foreground and background models, dynamic location models, and pairwise terms of label smoothness potentials. We perform extensive quantitative and qualitative experiments on benchmark datasets. Our method achieves superior performance in comparison to the current state-of-the-art in terms of accuracy and speed.
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