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Saliency Detection via Diversity-Induced Multi-view Matrix Decomposition

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10111))

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Abstract

In this paper, a diversity-induced multi-view matrix decomposition model (DMMD) for salient object detection is proposed. In order to make the background cleaner, \(\mathrm {Schatten}\)-p norm with an appropriate value of p in (0,1] is used to constrain the background part. A group sparsity induced norm is imposed on the foreground (salient part) to describe potential spatial relationships of patches. And most importantly, a diversity-induced multi-view regularization based Hilbert-Schmidt Independence Criterion (HSIC), is employed to explore the complementary information of different features. The independence between the multiple features will be enhanced. The optimization problem can be solved through an augmented Lagrange multipliers method. Finally, high-level priors are merged to boom the salient regions detection. Experiments on the widely used MSRA-5000 dataset show that the DMMD model outperforms other state-of-the-art methods.

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Acknowledgement

This work is supported in part by the National Natural Science Funds of China (Grant Nos. 61472257, 61402290, 61401287) and in part by the Natural Science Foundation of Shenzhen under Grant JCYJ 20160307154003475 and 2016050617251253.

Author information

Authors and Affiliations

  1. College of Mathematics and Statistics, Shenzhen University, Shenzhen, China

    Xiaoli Sun & Zhixiang He

  2. School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China

    Xiujun Zhang

  3. College of Information Engineering, Shenzhen University, Shenzhen, China

    Wenbin Zou

  4. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    George Baciu

Authors
  1. Xiaoli Sun
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  2. Zhixiang He
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  3. Xiujun Zhang
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  4. Wenbin Zou
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  5. George Baciu
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Corresponding author

Correspondence to Xiaoli Sun .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Sun, X., He, Z., Zhang, X., Zou, W., Baciu, G. (2017). Saliency Detection via Diversity-Induced Multi-view Matrix Decomposition. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10111. Springer, Cham. https://doi.org/10.1007/978-3-319-54181-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-54181-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54180-8

  • Online ISBN: 978-3-319-54181-5

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