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Graph Transduction Learning of Object Proposals for Video Object Segmentation
[chapter]
2015
Lecture Notes in Computer Science
We propose an unsupervised video object segmentation algorithm that detects recurring objects and learns cohort object proposals over space-time. Our core contribution is a graph transduction process that learns object proposals densely over space-time, exploiting both appearance models learned from rudimentary detections of sparse objectlike regions, and their intrinsic structures. Our approach exploits the fact that rudimentary detections of recurring objects in video, despite appearance
doi:10.1007/978-3-319-16817-3_36
fatcat:mrzlxjps6rcwvau33gg23fkfh4