Abstract
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 grou** 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.
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References
Tron, R., Vidal, R.: A benchmark for the comparison of 3-d motion segmentation algorithms. In: CVPR (2007)
Rao, S.R., Tron, R., Vidal, R.: Motion segmentation via robust subspace seperation in the presence of outlying, incomplete, or corrupted trajectories. PAMI (2010)
DeMenthon, D., Megret, R.: Spatio-temporal segmentation of video by hierarchical mean shift analysis. In: CVPR (2000)
Khan, S., Shah, M.: Object based segmentation of video using color, motion and spatial information. PAMI (2005)
Torsello, A., Pavan, M., Pelillo, M.: Object based segmentation of video using color, motion and spatial information. In: EMMCVPR (2005)
Grundmann, M., Kwatra, V., Han, M., Essa, I.: Efficient hierarchical graph-based video segmentation. In: CVPR (2010)
Nagahashi, T., Fujiyoshi, H., Kanade, T.: Video Segmentation Using Iterated Graph Cuts Based on Spatio-temporal Volumes. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009, Part II. LNCS, vol. 5995, pp. 655–666. Springer, Heidelberg (2010)
Brendel, W., Todorovic, S.: Video object segmentation by tracking regions. In: ICCV (2009)
Ke, Q., Kanade, T.: A subspace approach to layer extraction. In: CVPR (2001)
Hedau, V., Arora, H., Ahuja, N.: Matching images under unstable segmentations. In: CVPR (2008)
Kim, T.H., Lee, K.M., Lee, S.U.: Learning full pairwise affinities for spectral segmentation. In: CVPR (2010)
Weiss, Y.: Segmentation using eigenvectors: a unifying view. In: ICCV (1999)
Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI (2000)
Cour, T., Bébézit, F., Shi, J.: Segmentation using eigenvectors: a unifying view. In: CVPR (2005)
Lin, F., Cohen, W.W.: Power iteration clustering. In: ICML (2010)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. PAMI (2002)
Kohli, P., Ladicky, L., Torr, P.: Robust higher order potentials for enforcing label consistency. In: CVPR (2008)
Hu, G., Gao, Q.: A non-parametric statistics based method for generic curve partition and classification. In: ICIP (2010)
Vazquez-Reina, A., Avidan, S., Pfister, H., Miller, E.: Multiple Hypothesis Video Segmentation from Superpixel Flows. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 268–281. Springer, Heidelberg (2010)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: From contours to regions:an empirical evaluation. In: CVPR (2009)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Action as space-time shapes. IEEE TPAMI (2007)
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Di, X., Chang, H., Chen, X. (2013). Multi-layer Spectral Clustering for Video Segmentation. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37444-9_1
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DOI: https://doi.org/10.1007/978-3-642-37444-9_1
Publisher Name: Springer, Berlin, Heidelberg
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