Abstract
Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Levinshtein, A., Sminchisescu, C., Dickinson, S.: Spatiotemporal Closure. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 369–382. Springer, Heidelberg (2011)
Levinshtein, A., Sminchisescu, C., Dickinson, S.: Optimal Contour Closure by Superpixel Grouping. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 480–493. Springer, Heidelberg (2010)
Sundaram, N., Keutzer, K.: Long term video segmentation through pixel level spectral clustering on gpus. In: ICCV Workshops (2011)
Maire, M., Arbelaez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural images. In: CVPR (2008)
Li, Y., Sun, J., Shum, H.Y.: Video object cut and paste. ACM Trans. Graph. (2005)
Tsai, D., Flagg, M., Rehg, J.M.: Motion coherent tracking with multi-label mrf optimization. In: BMVC (2010)
Criminisi, A., Cross, G., Blake, A., Kolmogorov, V.: Bilayer segmentation of live video. In: CVPR (1) (2006)
Lee, Y.J., Kim, J., Grauman, K.: Key-segments for video object segmentation. In: ICCV (2011)
Huang, Y., Liu, Q., Metaxas, D.N.: Video object segmentation by hypergraph cut. In: CVPR (2009)
Grundmann, M., Kwatra, V., Han, M., Essa, I.A.: Efficient hierarchical graph-based video segmentation. In: CVPR (2010)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision (2004)
Rubio, J.C., Serrat, J., López, A.M.: Unsupervised co-segmentation through region matching. In: CVPR (2012)
Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR (2010)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. (1998)
Kläer, A., Marszalek, M., Schmid, C.: A spatio-temporal descriptor based on 3d-gradients. In: BMVC 2008 (2008)
Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: CVPR (2010)
Tiburzi, F., Escudero, M., Bescós, J., Sanchez, J.M.M.: A ground truth for motion-based video-object segmentation. In: ICIP (2008), http://www-vpu.ii.uam.es/CVSG/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rubio, J.C., Serrat, J., López, A. (2013). Video Co-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_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-37444-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37443-2
Online ISBN: 978-3-642-37444-9
eBook Packages: Computer ScienceComputer Science (R0)