Cosaliency Detection in Images Using Structured Matrix Decomposition and Objectness Prior

  • Sayanti BardhanEmail author
  • Shibu Jacob
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)


Cosaliency detection methods typically fail to perform in the situation where the foreground has multiple components. This paper proposes a novel framework, Cosaliency via Structured Matrix Decomposition (CSMD), for efficient detection of cosalient objects. This paper addresses the issue of saliency detection for images with multiple components in the foreground, by proposing a superpixel level, objectness prior. In this paper, we further propose a novel fusion technique to combine objectness prior to the cosaliency object detection framework. The proposed model is evaluated on challenging benchmark cosaliency dataset that has multiple components in the foreground. It outperforms prominent state-of-the-art methods in terms of efficiency.


Cosaliency detection Matrix decomposition Fusion Objectness 



The authors thank the faculty and members of Visualization and Perception Lab, Department of Computer Science and Engineering, IIT-Madras, for the constant guidance and support for this work. The authors would also like to acknowledge Marine Sensor Systems Lab, NIOT and Director, NIOT for their support.


  1. 1.
    Chang, K.-Y., Liu, T.-L., Lai, S.-H.: From co-saliency to co-segmentation: an efficient and fully unsupervised energy minimization model. In: CVPR, pp. 2129–2136 (2011)Google Scholar
  2. 2.
    Huazhu, F., Cao, X., Zhuowen, T.: Cluster-based co-saliency detection. IEEE Trans. Image Process. 22(10), 3766–3778 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Tan, Z., Wan, L., Feng, W., Pun, C.-M.: Image co-saliency detection by propagating superpixel affinities. In: ICASSP, pp. 2114–2118 (2013)Google Scholar
  4. 4.
    Cao, X., Tao, Z., Zhang, B., Huazhu, F., Feng, W.: Self-adaptively weighted co-saliency detection via rank constraint. IEEE Trans. Image Process. 23(9), 4175–4186 (2014)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Chen, H.-T.: Preattentive co-saliency detection. In: ICIP, pp. 1117–1120 (2010)Google Scholar
  6. 6.
    Li, H., Meng, F., Ngan, K.: Co-salient object detection from multiple images. IEEE Trans. Multimed. 15(8), 1896–1909 (2013)Google Scholar
  7. 7.
    Li, H., Ngan, K.: A co-saliency model of image pairs. IEEE Trans. Image Process. 20(12), 3365–3375 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ye, L., Liu, Z., Li, J., Zhao, W.-L., Shen, L.: Co-saliency detection via co-salient object discovery and recovery. Signal Process. Lett. 22(11), 2073–2077 (2015)CrossRefGoogle Scholar
  9. 9.
    Li, L., Liu, Z., Zou, W., Zhang, X., and Le Meur, O.: Co-saliency detection based on region-level fusion and pixel-level refinement. In: ICME, pp. 1–6 (2014)Google Scholar
  10. 10.
    Chen, Y.L., Hsu, C.-T.: Implicit rank-sparsity decomposition: applications to saliency/co-saliency detection. In: ICPR, pp. 2305–2310 (2014)Google Scholar
  11. 11.
    Yan, J., Zhu, M., Liu, H., Liu, Y.: Visual saliency detection via sparsity pursuit. IEEE Signal Process. Lett. 17(8), 739–742 (2010)CrossRefGoogle Scholar
  12. 12.
    X. Shen and Y. Wu: A unified approach to salient object detection via low rank matrix recovery. In: CVPR, 2012, 2296–2303Google Scholar
  13. 13.
    Lang, C., Liu, G., Yu, J., Yan, S.: Saliency detection by multitask sparsity pursuit. IEEE Trans. Image Process. 21(3), 1327–1338 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zou, W., Kpalma, K., Liu, Z., Ronsin, J.: Segmentation driven low-rank matrix recovery for saliency detection. In: BMVC, pp. 1–13 (2013)Google Scholar
  15. 15.
    Candès, E., Li, X., Ma, Y., Wright, J.: Robust principal component analysis. J. ACM 58(3), 1–39 (2011)Google Scholar
  16. 16.
    Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013)CrossRefGoogle Scholar
  17. 17.
    Peng, H., Li, B., Ji, R., Hu, W., Xiong, W., Lang, C.: Salient object detection via low-rank and structured sparse matrix decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 818–832 (2017)CrossRefGoogle Scholar
  18. 18.
    Li, Y., Fu, K., Liu, Z., Yang, J.: Efficient saliency-model-guided visual co-saliency detection. IEEE Signal Process. Lett. 22(5), 588–592 (2014)CrossRefGoogle Scholar
  19. 19.
    Zhang, D., Fu, H., Han, J., Wu, F.: A review of co-saliency detection technique: fundamentals, applications, and challenge 1–16 (2016). arXiv:1604.07090
  20. 20.
    Li, L., Liu, Z., Zou, W., Zhang, X., Le Meur, O.: Co-saliency detection based on region-level fusion and pixel-level refinement. In: ICME, pp. 1–6 (2014)Google Scholar
  21. 21.
    Liu, Z., Zou, W., Li, L., Shen, L., LeMeur, O.: Co-saliency detection based on hierarchical segmentation. IEEE Signal Process. Lett. 21(1), 88–92 (2014)CrossRefGoogle Scholar
  22. 22.
    Feichtinger, H.G., Strohmer, T.: Gabor analysis and algorithms: theory and applications. Springer, Berlin (1998)Google Scholar
  23. 23.
    Simoncelli, E.P., Freeman, W.T.: The steerable pyramid: a flexible architecture for multi-scale derivative computation. In: ICIP, pp. 444–447 (1995)Google Scholar
  24. 24.
    Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: CVPR, pp. 2296–2303 (2012)Google Scholar
  25. 25.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., S̈usstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)Google Scholar
  26. 26.
    Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59(2), 167–181 (2004)CrossRefGoogle Scholar
  27. 27.
    Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: CVPR, pp. 2814–2821 (2014)Google Scholar
  28. 28.
    Xu, B., Bu, J., Chen, C., Cai, D., He, X., Liu, W., Luo, J.: Efficient manifold ranking for image retrieval. In: ACM SIGIR, pp. 525–534 (2011)Google Scholar
  29. 29.
    Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2189–2202 (2012)CrossRefGoogle Scholar
  30. 30.
    Sun, J., Ling, H.: Scale and object aware image thumbnailing. Int. J. Comput. Vision 104(2), 135–153 (2013)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Li, Y., Fu, K., Zhou, L., Qiao, Y., Yang, J., Li, B.: Saliency detection based on extended boundary prior with foci of attention. In: ICASSP, pp. 2798–2802 (2014)Google Scholar
  32. 32.
    Roy, S., Das, S.: Multi-criteria energy minimization with boundedness, edge-density and rarity, for object saliency in natural images. In: ICVGIP 2014, 55:1–55:8 (2014) Google Scholar
  33. 33.
    Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T.: Icoseg: interactive co-segmentation with intelligent scribble guidance. In: CVPR, pp. 3169–3176 (2010)Google Scholar
  34. 34.
    Roy, S., Das, S.: Saliency detection in images using graph-based rarity, spatial compactness and background prior. In: VISAPP, pp. 523–530 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Visualization and Perception Lab, Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia
  2. 2.Marine Sensor SystemsNational Institute of Ocean TechnologyChennaiIndia

Personalised recommendations