This paper presents a novel approach for motion segmentation by using strategies of splitting and remerging. The presented approach, Mossar, hybridizes two existing ones to obtain their potential advantages while covering weaknesses: (1) velocity-based, one of the most widely used approaches that has fairly low accuracy but provides computational simplicity and (2) graph-based, a state-of-the-art approach that provides outstanding accuracy, yet bears high computational complexity and a burden in setting of thresholds. An initial set of key frames is generated by a velocity-based splitting process and then fed into a graph-based remerging process for refinement. We present mechanisms that improve key-frames capturing in the velocity-based approach as well as details on how the graph-based approach is modified and later applied to remerging. The proposed approach also allows users to interactively add or reduce the number of key frames to control segmentation hierarchy without the need to change threshold values and re-run segmentation, as usually done in existing approaches. Our experimental results show that the presented hybrid approach, compared to both velocity-based and graph-based, demonstrates superior performance in terms of accuracy and in comparison to graph-based, our approach has not only less complexity but also a lesser number of thresholds, the values of which can be much more simply determined.
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ground truth: a set of key frames located at where the data should be partitioned to create segments
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The authors wish to thank Professor Kingkarn Sookhanaphibarn, Bangkok University, for her assistance in the technical editing of the manuscript. We would also like to express special thanks to the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.
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Paliyawan, P., Choensawat, W. & Thawonmas, R. Mossar: motion segmentation by using splitting and remerging strategies. Multimed Tools Appl 77, 27761–27788 (2018). https://doi.org/10.1007/s11042-018-5965-x
- Motion segmentation
- Motion representation
- Graph Kernel matching