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Saliency Optimization Integrated Robust Background Detection with Global Ranking

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Intelligence Science and Big Data Engineering. Visual Data Engineering (IScIDE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11935))

Abstract

Image saliency detection plays an important role in the field of computer vision. In order to make the saliency detection achieve better results, this paper proposes an algorithm that combines the global cue with the robust background prior measurement. Specifically, we first get a global ranking of superpixels by absorbing time in Markov chain, which is the absorbing nodes represent the superpixels of the fictitious boundary, and the transient nodes denote the others. Then, the global similarity of transient node can be measured by its absorbing time, so a global ranking for each transient node can be calculated, which is called as global cue in this paper. Finally, considering the remarkable energy optimization model, we integrate the robust background prior measurement with calculated global cue to form a new optimization model for saliency detection. In conclusion, our method is better than some typical significant detection algorithms on several datasets through the experimental verification.

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References

  1. Wang, L., Xue, J., Zheng, N., Hua, G.: Automatic salient object extraction with contextual cue. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 105–112. IEEE (2011). https://dl.acm.org/citation.cfm?doid=2009916.2009934

  2. Marchesotti, L., Cifarelli, C., Csurka, G.: A framework for visual saliency detection with applications to image thumbnailing. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2232–2239. IEEE (2009). https://ieeexplore.ieee.org/document/5459467

  3. Navalpakkam, V., Itti, L.: An integrated model of top-down and bottom-up attention for optimizing detection speed. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2049–2056. IEEE (2006). https://ieeexplore.ieee.org/document/1641004

  4. Ma, Y.F., Lu, L., Zhang, H.J., Li, M.: A user attention model for video summarization. In: Proceedings of the ACM International Conference on Multimedia, pp. 533–542. ACM (2002). https://dl.acm.org/citation.cfm?doid=641007.641116

  5. Borji, A., Sihite, D.N., Itti, L.: Probabilistic learning of task-specific visual attention. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 470–477. IEEE (2012). https://ieeexplore.ieee.org/document/6247710

  6. Itti, L., Koch, C.: Feature combination strategies for saliency-based visual attention systems. J. Electron. Imaging 10(1), 161–170 (2001)

    Article  Google Scholar 

  7. Gao, D., Vasconcelos, N.: Discriminant saliency for visual recognition from cluttered scenes. In: Advances in Neural Information Processing Systems, pp. 481–488 (2005)

    Google Scholar 

  8. Frintrop, S., Backer, G., Rome, E.: Goal-directed search with a top-down modulated computational attention system. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 117–124. Springer, Heidelberg (2005). https://doi.org/10.1007/11550518_15

    Chapter  Google Scholar 

  9. Kanan, C., Tong, M.H., Zhang, L., Cottrell, G.W.: SUN: top-down saliency using natural statistics. Vis. Cogn. 17(6–7), 979–1003 (2009). ttps://www.tandfonline.com/doi/abs/10.1080/13506280902771138

    Article  Google Scholar 

  10. Alexe, B., Deselaers, T., Ferrari, V.: What is an object?. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 73–80. IEEE (2010). https://ieeexplore.ieee.org/document/5540226

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

    Google Scholar 

  12. Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849–856 (2002)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  16. Xiao, Y., Wang, L., Jiang, B., Tu, Z., Tang, J.: A global and local consistent ranking model for image saliency computation. J. Vis. Commun. Image Represent. 46, 199–207 (2017)

    Article  Google Scholar 

  17. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  18. Liu, T., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)

    Article  Google Scholar 

  19. Movahedi, V., Elder, J.H.: Design and perceptual validation of performance measures for salient object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition-Workshops, pp. 49–56. IEEE (2010). https://ieeexplore.ieee.org/document/5543739

  20. Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 29–42. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33712-3_3

    Chapter  Google Scholar 

  21. Tong, N., Lu, H., Zhang, L., Ruan, X.: Saliency detection with multi-scale superpixels. IEEE Signal Process. Lett. 21(9), 1035–1039 (2014)

    Article  Google Scholar 

  22. Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604. IEEE (2009). https://ieeexplore.ieee.org/document/5206596

  23. Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–740. IEEE (2012)

    Google Scholar 

  24. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, pp. 545–552 (2007)

    Google Scholar 

  25. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

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

    Google Scholar 

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Acknowledgments

This work was supported by the Key Natural Science Project of Anhui Provincial Education Department (KJ2018A0023), the Guangdong Province Science and Technology Plan Projects (2017B010110011), the Anhui Key Research and Development Plan (1804a09020101), the National Basic Research Program (973 Program) of China (2015CB351705), the National Natural Science Foundation of China (61906002, 61402002 and 61876002) and 2018 College Students Innovation and Entrepreneurship Training Program (201810357353).

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Correspondence to Dengdi Sun .

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Zhang, Z., Liang, Y., Zheng, J., Li, K., Ding, Z., Sun, D. (2019). Saliency Optimization Integrated Robust Background Detection with Global Ranking. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_43

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  • DOI: https://doi.org/10.1007/978-3-030-36189-1_43

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  • Online ISBN: 978-3-030-36189-1

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