Video object segmentation with shortest path
Proceedings of the 20th ACM international conference on Multimedia
Moving object segmentation using motor signals
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Streaming hierarchical video segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Video segmentation with superpixels
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Online learning for fast segmentation of moving objects
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
User assisted disparity remapping for stereo images
Image Communication
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We present an approach to discover and segment foreground object(s) in video. Given an unannotated video sequence, the method first identifies object-like regions in any frame according to both static and dynamic cues. We then compute a series of binary partitions among those candidate "key-segments" to discover hypothesis groups with persistent appearance and motion. Finally, using each ranked hypothesis in turn, we estimate a pixel-level object labeling across all frames, where (a) the foreground likelihood depends on both the hypothesis's appearance as well as a novel localization prior based on partial shape matching, and (b) the background likelihood depends on cues pulled from the key-segments' (possibly diverse) surroundings observed across the sequence. Compared to existing methods, our approach automatically focuses on the persistent foreground regions of interest while resisting oversegmentation. We apply our method to challenging benchmark videos, and show competitive or better results than the state-of-the-art.