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To overcome this bottleneck, we design a scaling up method. We segment video into several spatial-temporal volumes, which are actually very coarse segmentations. Next, we segment each volume using Algorithm 1. Performing multiple passes over the disjoint volumes, we can obtain an identical result with the original algorithm. 3 Experimental Results This section presents our quantitative and qualitative evaluation results of the proposed method on a wide range of videos. As for parameter setting, the initial mean shift segmentation parameters and the number of total segments K are chosen empirically according to the video contents.
In: ICCV Workshops (2011) 4. : Using contours to detect and localize junctions in natural images. In: CVPR (2008) 5. : Video object cut and paste. ACM Trans. Graph. (2005) 6. : Motion coherent tracking with multi-label mrf optimization. In: BMVC (2010) 7. : Bilayer segmentation of live video. In: CVPR (1) (2006) 8. : Key-segments for video object segmentation. In: ICCV (2011) 9. : Video object segmentation by hypergraph cut. In: CVPR (2009) 10. : Eﬃcient hierarchical graphbased video segmentation.
Our segmentation retains more important details without losing the overall meaning of the scene: top of the chimney and windows in House, stand in Kwan and the person and background in Walking. Looking at the walking person further, we segment the whole person out, but  merges face with background and  segment the person into upper and lower body. When it is to the background, our method gives more sound regions of wall and road without outthrust in the results of the other two. Multi-layer Spectral Clustering for Video Segmentation 9 Fig.
Objectif lune by Hergé