Advertisement

Causal Video Segmentation Using Superseeds and Graph Matching

  • Vijay N. Gangapure
  • Susmit Nanda
  • Ananda S. Chowdhury
  • Xiaoyi Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9069)

Abstract

The goal of video segmentation is to group pixels into meaningful spatiotemporal regions that exhibit coherence in appearance and motion. Causal video segmentation methods use only past video frames to achieve the final segmentation. The problem of causal video segmentation becomes extremely challenging due to size of the input, camera motion, occlusions, non-rigid object motion, and uneven illumination. In this paper, we propose a novel framework for semantic segmentation of causal video using superseeds and graph matching. We first employ SLIC for the extraction of superpixels in a causal video frame. A set of superseeds is chosen from the superpixels in each frame using color and texture based spatial affinity measure. Temporal coherence is ensured through propagation of labels of the superseeds across each pair of adjacent frames. A graph matching procedure based on comparison of the eigenvalues of graph Laplacians is employed for label propagation. Watershed algorithm is applied finally to label the remaining pixels to achieve final segmentation. Experimental results clearly indicate the advantage of the proposed approach over some recently reported works.

Keywords

Causal video segmentation Superseeds Spatial affinity Graph matching 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE TPAMI 24, 603–619 (2002)CrossRefGoogle Scholar
  2. 2.
    Lee, Y.J., Kim, J., Grauman, K.: Key-Segments for Video Object Segmentation. In: ICCV, pp. 1995–2002 (2011)Google Scholar
  3. 3.
    Couprie, C., Farabet, C., LeCun, Y., Najman, L.: Causal Graph-Based Video Segmentation. In: ICIP, pp. 4249–4253 (2013)Google Scholar
  4. 4.
    Miksik, O., Munoz, D., Bagnell, J.A.D., Hebert, M.: Efficient Temporal Consistency for Streaming Video Scene Analysis. Tech. Report CMU-RI-TR-12-30, Robotics Institute, Pittsburgh, PA (2012)Google Scholar
  5. 5.
    Paris, S.: Edge-preserving smoothing and mean-shift segmentation of video streams. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 460–473. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Galasso, F., Cipolla, R., Schiele, B.: Video Segmentation with Superpixels. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 760–774. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Kumar, M.P., Torr, P., Zisserman, A.: Learning Layered Motion Segmentations of Video. In: ICCV, pp. 301–319 (2012)Google Scholar
  8. 8.
    Galasso, F., Iwasaki, M., Nobori, K., Cipolla, R.: Spatio-temporal Clustering of Probabilistic Region Trajectories. In: ICCV, pp. 301–319 (2011)Google Scholar
  9. 9.
    Grundmann, M., Kwatra, V., Han, M., Essa, I.: Efficient Hierarchical Graph-Based Video Segmentation. In: ICPR, pp. 2141–2148 (2010)Google Scholar
  10. 10.
    Ferreira de Souza, K.J., Arajo, A.A., Patrocnio Jr., Z.K.G., Guimares, S.J.F.: Graph-based Hierarchical Video Segmentation Based on a Simple Dissimilarity Measure. Pattern Recognition Letters 47, 85–92 (2014)CrossRefGoogle Scholar
  11. 11.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC Superpixels Compared to State-of-the-art Superpixels Methods. IEEE TPAMI 34, 2274–2281 (2012)CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE TPAMI 24, 971–987 (2002)CrossRefGoogle Scholar
  13. 13.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters, pp. 226–231. AAAI Press (1996)Google Scholar
  14. 14.
    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Koutra, D., Parikh, A., Ramdas, A., Xiang, J.: Algorithms for Graph Similarity and Subgraph Matching. Tech. Report CMU (2011)Google Scholar
  16. 16.
    Meyer, F.: Topographic Distance and Watershed Lines. Signal Processing 38, 113–125 (1994)CrossRefzbMATHGoogle Scholar
  17. 17.
    Cousty, J., Bertrand, G., Najman, L., Couprie, M.: Watershed Cuts: Minimum Spanning Forests and The Drop of Water Principle. IEEE TPAMI 31(8), 1362–1374 (2009)CrossRefGoogle Scholar
  18. 18.
    Csurka, G., Larlus, D., Perronnin, F.: What Is a Good Evaluation Measure for Semantic Segmentation? BMVC, 2013/027 (2013)Google Scholar
  19. 19.
    Roerdink, J.B.T.M., Meijster, A.: The Watershed Transform: Definitions, Algorithms and Parallelization Strategies. Fundamenta Informaticae 41, 187–228 (2001)MathSciNetGoogle Scholar
  20. 20.
  21. 21.
    Zhou Y., Bai X., Liu W., and Latecki L.J.: Fusion With Diffusion for Robust Visual Tracking. The Neural Information Processing Systems (NIPS), 2987–2995 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vijay N. Gangapure
    • 1
  • Susmit Nanda
    • 1
  • Ananda S. Chowdhury
    • 1
  • Xiaoyi Jiang
    • 2
  1. 1.Department of Electronics and Telecommunication Engg.Jadavpur UniversityKolkataIndia
  2. 2.Department of Mathematics and Computer ScienceUniversity of MünsterMünsterGermany

Personalised recommendations