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Hierarchical frame structure based interactive video object cutout

Published:11 December 2011Publication History

ABSTRACT

Video object cutout aims to extract homogenous objects from background in a video clip, which is a key process in many video processing fields, such as video compositing, video stylized rendering and so on. In this paper, we present a novel video cutout method by matching hierarchical structure of video frames. We first segment each frame by mean shift and construct hierarchical structure as a tree in preprocess stage. We then require user's interaction to label objects in a key frame. We further model video segmentation as matching hierarchical structure of frame and proposed an inter-frame matching algorithm. Experimental results show that our method can achieve desirable video segmentation results.

References

  1. Bai, X., Wang, J., Simons, D., and Sapiro, G. 2009. Video snapcut: Robust video object cutout using localized claasifiers. ACM Transactions on Graphics 28, 3 (July), 243--248. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Chuang, Y., Agarwala, A., Curless, B., Salesin, D., and Szeliski, R. 2002. Video matting of complex scenes. ACM Transactions on Graphics 21, 3 (July), 243--248. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Collomosse, J., Rowntree, D., and Hall, P. 2005. Stroke surfaces: Temporally coherent artistic animations from video. IEEE Transactions on Visualiztion and Computer Graphics 11, 4 (July/August), 540--549. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Comaniciu, D., and Meer, P. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 5 (May), 603--619. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Comaniciu, D. 2002. Image segmentation using clustering with saddle point detection. In Proceedings of International Conference on Image Processing, ICIP.Google ScholarGoogle ScholarCross RefCross Ref
  6. DeMenthon, D. 2002. Spatio-temporal segmentation of video by hierarchical mean shift analysis. In Proceedings of the Statistical Methods in Video Processing Workshop, Monash Univ. Copenhagen, Denmark.Google ScholarGoogle Scholar
  7. Li, Y., Sun, J., and Shum, H. 2005. Video object cut and paste. ACM Transactions on Graphics 24, 3 (July), 595--600. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Paris, S., and Durand, F. 2007. A topological approach to hierarchical segmentation using mean shift. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 1978--1985.Google ScholarGoogle Scholar
  9. Wang, J., Thiesson, B., Xu, Y., and Cohen, M. 2004. Image and video segmentation by anisotropic kernel mean shift. In Proceedings of European Conference on Computer Vision, ECCV.Google ScholarGoogle Scholar
  10. Wang, J., Xu, Y., Shum, H., Agrawala, M., and Cohen, M. 2004. Video tooning. ACM Transactions on Graphics 23, 3 (July), 574--583. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Wang, J., Bhat, P., Colburn, R., Agrawala, M., and Cohen, M. 2005. Interactive video cutout. ACM Transactions on Graphics 24, 3 (July), 585--594. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        • Published in

          cover image ACM Conferences
          VRCAI '11: Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
          December 2011
          617 pages
          ISBN:9781450310604
          DOI:10.1145/2087756

          Copyright © 2011 ACM

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          New York, NY, United States

          Publication History

          • Published: 11 December 2011

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