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Saliency Detection Optimization via Modified Secondary Manifold Ranking and Blurring Depression

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

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Abstract

We propose an unsupervised saliency optimization method mainly via modified secondary manifold ranking and blurring depression (SMBD). Generally, saliency object is detected insufficiently by most methods. To solve this problem, a modified manifold ranking is circulated twice to detect saliency object completely. A blurry degree detection approach is introduced to locate blurring regions, which is more likely to be background. As a result, blurring regions are depressed by SMBD to avoid mistaking background as foreground. Our method is performed based on hierarchical luminance for better performance. Extensive experimental results demonstrate that SMBD is able to promote the performances of state-of-the-art saliency detection algorithms significantly.

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References

  1. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1254–1259 (1998)

    Article  Google Scholar 

  2. Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.M.: Global contrast based salient region detection. In: Proceedings of Computer Vision and Pattern Recognition, pp. 409–416 (2015)

    Google Scholar 

  3. Liu, T., Sun, J., Zheng, N.N., Tang, X., Shum, H.Y.: Learning to detect a salient object. In: Proceedings of Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  4. Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: Proceedings of Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)

    Google Scholar 

  5. Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: Proceedings of Computer Vision and Pattern Recognition, pp. 2814–2821 (2014)

    Google Scholar 

  6. Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: Proceedings of Computer Vision and Pattern Recognition, pp. 110–119 (2015)

    Google Scholar 

  7. Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing Markov chain. In: Proceedings of International Conference on Computer Vision, pp. 1665–1672 (2013)

    Google Scholar 

  8. Liu, Z., Zou, W., Meur, O.L.: Saliency tree: a novel saliency detection framework. IEEE Trans. Image Process. 23, 1937–1952 (2014)

    Article  MathSciNet  Google Scholar 

  9. Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 2083–2090 (2014)

    Google Scholar 

  10. Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: Proceedings of Computer Vision and Pattern Recognition, pp. 1265–1274 (2015)

    Google Scholar 

  11. Wang, L., Lu, H., Xiang, R., Yang, M.H.: Deep networks for saliency detection via local estimation and global search. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 3183–3192 (2015)

    Google Scholar 

  12. Lempitsky, V., Kohli, P., Rother, C., Sharp, T.: Image segmentation with a bounding box prior. In: Proceedings of International Conference on Computer Vision, pp. 277–284 (2009)

    Google Scholar 

  13. Marchesotti, L., Cifarelli, C., Csurka, G.: A framework for visual saliency detection with applications to image thumbnailing. In: Proceedings of International Conference on Computer Vision, pp. 2232–2239 (2009)

    Google Scholar 

  14. Kanan, C., Tong, M.H., Zhang, L., Cottrell, G.W.: Sun: top-down saliency using natural statistics. Vis. Cogn. 17, 979–1003 (2009)

    Article  Google Scholar 

  15. Yang, M.H., Yang, J.: Top-down visual saliency via joint CRF and dictionary learning. In: Proceedings of Computer Vision and Pattern Recognition, pp. 2296–2303 (2012)

    Google Scholar 

  16. Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: Proceedings of Computer Vision and Pattern Recognition, pp. 1155–1162 (2013)

    Google Scholar 

  17. Borji, A.: What is a salient object? A dataset and a baseline model for salient object detection. Trans. Image Process. 24, 742–756 (2014)

    Article  Google Scholar 

  18. Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008). doi:10.1007/978-3-540-79547-6_7

    Chapter  Google Scholar 

  19. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality aassessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation of China(Grant Nos. 11627802, 51678249), by the Science and Technology Projects of Guangdong (2013A011403003), and by the Science and Technology Projects of Guangzhou (201508010023).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

    Han Liu, Bo Li & Hang Gao

Authors
  1. Han Liu
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  2. Bo Li
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  3. Hang Gao
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Corresponding author

Correspondence to Bo Li .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Liu, H., Li, B., Gao, H. (2017). Saliency Detection Optimization via Modified Secondary Manifold Ranking and Blurring Depression. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_30

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  • DOI: https://doi.org/10.1007/978-3-319-59081-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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