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A hierarchical visual saliency detection method by combining distinction and background probability maps

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Abstract

We propose a bottom-up method for image saliency detection by combining the distinction map and the background probability map. The contributions of our work are as follows. First, a novel distinction measure is proposed by weighted combination of the color contrast and the spatial distance distribution criterions in previous works. Second, a background pixel distribution approximation method using patches sampled near the image borders is introduced. Finally, the distinction map and the background probability map are incorporated into a hierarchical framework to generate the final saliency map. We have compared our method with several recent works experimentally and observe that competitive results can be achieved.

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References

  1. Rudoy, D., Goldman, D.B., Shechtman, E., Zelnik-Manor, L.: Learning Video saliency from human gaze using candidate selection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1147–1154 (2013)

  2. Liu, H., Heynderickx, I.: Studying the added value of visual attention in objective image quality metrics based on eye movement data. Proceedings of the IEEE International Conference on Image Processing, pp. 3097–3100 (2009)

  3. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  4. Achanta, R., Hemami, S.S., Estrada, F.J., Süsstrunk, S.: Frequency-tuned salient region detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1597–1604 (2009)

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

  6. Siva, P., Russell, C., Xiang, T., de Agapito, L.: Looking beyond the image: unsupervised learning for object saliency and detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3238–3245 (2013)

  7. Jimei, Y., Yang, M.H.: Top-down visual saliency via joint crf and dictionary learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2296–303 (2012)

  8. Borji, A.: Boosting bottom-up and top-down visual features for saliency estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 438–445 (2012)

  9. Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing markov chain. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1665–1672 (2013)

  10. Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global contrast based salient region detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 409–416 (2011)

  11. Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1536 (2013)

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

  13. Xiaohui, S., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 853–860 (2012)

  14. Borji, A., Itti, L.: Exploiting local and global patch rarities for saliency detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 478–485 (2012)

  15. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007)

  16. Hou, X., Harel, J., Koch, C.: Image signature: highlighting sparse salient regions. IEEE Trans Pattern Anal Mach Intell 34, 194–201 (2012)

    Article  Google Scholar 

  17. Xie, Y., Huchuan, L., Yang, M.H.: Bayesian saliency via low and mid level cues. IEEE Trans Image Process 22(5), 1689–1698 (2013)

    Article  MathSciNet  Google Scholar 

  18. Shen Xiaohui, Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 853–860 (2012)

  19. Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct? Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1139–1146 (2013)

  20. 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 

  21. Achanta, R., Estrada, F.: Salient region detection and segmentation. Proceedings of the International Conference on Computer Vision Systems, pp. 66–75 (2008)

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

  23. Zhang, J., Sclaroff, S.: Saliency detection: a Boolean map approach. Proceedings of the IEEE International Conference on Computer Vision (2013)

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Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Sanyuan Zhao, Jianbing Shen & Fengxia Li

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  1. Sanyuan Zhao
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  2. Jianbing Shen
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  3. Fengxia Li
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Correspondence to Sanyuan Zhao.

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Zhao, S., Shen, J. & Li, F. A hierarchical visual saliency detection method by combining distinction and background probability maps. Multimedia Systems 23, 343–350 (2017). https://doi.org/10.1007/s00530-015-0490-5

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  • DOI: https://doi.org/10.1007/s00530-015-0490-5

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