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On Multiple Image Group Cosegmentation

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Book cover Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9006))

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Abstract

The existing cosegmentation methods use intra-group information to extract a common object from a single image group. Observing that in many practical scenarios there often exist multiple image groups with distinct characteristics but related to the same common object, in this paper we propose a multi-group image cosegmentation framework, which not only discoveries intra-group information within each image group, but also transfers the inter-group information among different groups so as to more accurate object priors. Particularly, we formulate the multi-group cosegmentation task as an energy minimization problem. Markov random field (MRF) segmentation model and dense correspondence model are used in the model design and the Expectation-Maximization algorithm (EM) is adapted to solve the optimization. The proposed framework is applied on three practical scenarios including image complexity based cosegmentation, multiple training group cosegmentation and multiple noise image group cosegmentation. Experimental results on four benchmark datasets show that the proposed multi-group image cosegmentation framework is able to discover more accurate object priors and significantly outperform state-of-the-art single-group image cosegmentation methods.

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References

  1. Chai, Y., Rahtu, E., Lempitsky, V., Van Gool, L., Zisserman, A.: TriCoS: a tri-level class-discriminative co-segmentation method for image classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 794–807. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  2. Rother, C., Kolmogorov, V., Minka, T., Blake, A.: Cosegmentation of image pairs by histogram matching-incorporating a global constraint into MRFs. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 993–1000 (2006)

    Google Scholar 

  3. Zhu, H., Lu, J., Cai, J., Zheng, J., Thalmann, N.M.: Multiple foreground recognition and cosegmentation: an object-oriented CRF model with robust higher-order potentials. In: IEEE Winter Conference on Applications of Computer Vison (2014)

    Google Scholar 

  4. Mukherjee, L., Singh, V., Dyer, C.R.: Half-integrality based algorithms for cosegmentation of images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2028–2035 (2009)

    Google Scholar 

  5. Hochbaum, D.S., Singh, V.: An efficient algorithm for co-segmentation. In: International Conference on Computer Vision, pp. 269–276 (2009)

    Google Scholar 

  6. Vicente, S., Kolmogorov, V., Rother, C.: Cosegmentation revisited: models and optimization. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 465–479. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Collins, M., Xu, J., Grady, L., Singh, V.: Random walks based multi-image cosegmentation: quasiconvexity results and GPU-based solutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1656–1663 (2012)

    Google Scholar 

  8. Meng, F., Li, H., Liu, G., Ngan, K.N.: Image cosegmentation by incorporating color reward strategy and active contour model. IEEE Trans. Cybern. 43, 725–737 (2013)

    Article  Google Scholar 

  9. Joulin, A., Bach, F., Ponce, J.: Discriminative clustering for image co-segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1943–1950 (2010)

    Google Scholar 

  10. Chai, Y., Lempitsky, V., Zisserman, A.: BiCoS: a bi-level co-segmentation method for image classification. In: International Conference on Computer Vision, pp. 2579–2586 (2011)

    Google Scholar 

  11. Kim, G., Xing, E.P., Fei-Fei, L., Kanade, T.: Distributed cosegmentation via submodular optimization on anisotropic diffusion. In: International Conference on Computer Vision, pp. 169–176 (2011)

    Google Scholar 

  12. Kim, G., Xing, E.P.: On multiple foreground cosegmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  13. Joulin, A., Bach, F., Ponce, J.: Multi-class cosegmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 542–549 (2012)

    Google Scholar 

  14. Vicente, S., Rother, C., Kolmogorov, V.: Object cosegmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2217–2224 (2011)

    Google Scholar 

  15. Mukherjee, L., Singh, V., Peng, J.: Scale invariant cosegmentation for image groups. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1881–1888 (2011)

    Google Scholar 

  16. Rubio, J., Serrat, J., L\({\rm \acute{o}}\)pez, A., Paragios, N.: Unsupervised co-segmentation through region matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 749–756 (2012)

    Google Scholar 

  17. Li, H., Ngan, K.N.: A co-saliency model of image pairs. IEEE Trans. Image Process. 20, 3365–3375 (2011)

    Article  MathSciNet  Google Scholar 

  18. Ma, T., Latecki, L.J.: Graph transduction learning with connectivity constraints with application to multiple foreground cosegmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  19. Wang, F., Huang, Q., Guibas, L.J.: Image co-segmentation via consistent functional maps. In: IEEE International Conference on Computer Vision (ICCV), pp. 849–856. IEEE (2013)

    Google Scholar 

  20. Xing, G.K.E.P.: Jointly aligning and segmenting multiple web photo streams for the inference of collective photo storylines. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  21. Meng, F., Li, H., Ngan, K.N., Zeng, L., Wu, Q.: Feature adaptive co-segmentation by complexity awareness. IEEE Trans. Image Process. 22, 4809–4824 (2013)

    Article  MathSciNet  Google Scholar 

  22. Liu, C., Yuen, J., Torralba, A.: Sift flow: dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 33, 978–994 (2011)

    Article  Google Scholar 

  23. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012)

    Article  Google Scholar 

  24. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)

    Article  Google Scholar 

  25. Batra, D., Kowdle, A., Parikh, D.: iCoseg: interactive co-segmentation with intelligent scribble guidance. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3169–3176 (2010)

    Google Scholar 

  26. Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology (2010)

    Google Scholar 

  27. Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.V.: Cats and dogs. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  28. Rubinstein, M., Joulin, A., Kopf, J., Liu, C.: Unsupervised joint object discovery and segmentation in internet images. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

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Acknowledgement

This work was supported in part by the Major State Basic Research Development Program of China (973 Program 2015CB351804), NSFC (No. 61271289), the Singapore National Research Foundation under its IDM Futures Funding Initiative and administered by the Interactive & Digital Media Programme Office, Media Development Authority, the Ph.D. Programs Foundation of Ministry of Education of China (No. 20110185110002), and by The Program for Young Scholars Innovative Research Team of Sichuan Province, China (No. 2014TD0006).

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Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Fanman Meng & Hongliang Li

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Jianfei Cai

Authors
  1. Fanman Meng
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  2. Jianfei Cai
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  3. Hongliang Li
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Corresponding author

Correspondence to Jianfei Cai .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Meng, F., Cai, J., Li, H. (2015). On Multiple Image Group Cosegmentation. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-16817-3_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16816-6

  • Online ISBN: 978-3-319-16817-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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