Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Subspace Approach to Layer Extraction
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple hypothesis video segmentation from superpixel flows
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Video segmentation using iterated graph cuts based on spatio-temporal volumes
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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Video segmentation with spatial priority suffers from incoherence problem, since the presegments of consecutive frames may be very different. To address this problem, this paper proposes an effective and scalable approach for video segmentation, aiming to cluster video pixels that are coherent in both appearance and motion. We build up a multi-layer graph based on multiple segmentations of the video frames, where each presegment corresponds to a vertex in the graph and each layer corresponds to the segmentation result using mean shift algorithm under specific granularity. Three types of edges are connected in the graph and the corresponding affinities are defined which convey local grouping cues of intra-frame, inter-frame and inter-layer neighborhoods. Then the task of video segmentation is formulated into graph partition, which can be solved efficiently by power iteration clustering algorithm. Both qualitative and quantitative experimental results demonstrate the efficacy of our proposed method.