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International Conference on Intelligent Computing

ICIC 2015: Advanced Intelligent Computing Theories and Applications pp 243–250Cite as

Visual Saliency Detection Based on Color Contrast and Distribution

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9227))

Abstract

This paper is concerned with color contrast and distribution for detecting salient regions. First, in order to improve the computational efficiency and reduce the disturbance of noise, the input image is pre-segmented into superpixels. Next, color contrast features are considered in Lab color space and opponency color space. The color distances between a superpixel and other superpixels are calculated, but we do not choose all superpixels to participate the difference. In the meanwhile, the distribution feature is shown by considering the rarity and position of pixels. Finally, we select 2D entropy to measure the performance of salient maps, and select the proper features to fuse. Experimental results show that the proposed method outperforms the state-of-the-art methods on salient region detection.

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References

  1. Frintrop, S.: VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search. Springer, Heidelberg (2006)

    Book  Google Scholar 

  2. Navalpakkam, V., Itti, L.: An integrated model of top-down and bottom-up attention for optimizing detection speed. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2049–2056. IEEE (2006)

    Google Scholar 

  3. Fritz, G., Seifert, C., Paletta, L., Bischof, H.: Attentive object detection using an information theoretic saliency measure. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G.W. (eds.) WAPCV 2004. LNCS, vol. 3368, pp. 29–41. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Butko, N.J., Movellan, J.R.: Optimal scanning for faster object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2751–2758. IEEE (2009)

    Google Scholar 

  5. Judd, T., Ehinger, K., Durand, F., et al.: Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2106–2113. IEEE (2009)

    Google Scholar 

  6. Salah, A.A., Alpaydin, E., Akarun, L.: A selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 420–425 (2002)

    Article  Google Scholar 

  7. Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Netw. 19(9), 1395–1407 (2006)

    Article  Google Scholar 

  8. Han, S., Vasconcelos, N.: Biologically plausible saliency mechanisms improve feedforward object recognition. Vis. Res. 50(22), 2295–2307 (2010)

    Article  Google Scholar 

  9. Tsai, Y.: Hierarchical salient point selection for image retrieval. Pattern Recognit. Lett. 33(12), 1587–1593 (2012)

    Article  Google Scholar 

  10. Lin, L., Shan, J., Zheng, Z.: Partial-duplicate image retrieval via saliency-guided visually matching. IEEE Trans. Multimedia 20(3), 13–23 (2013)

    Article  Google Scholar 

  11. Bai, X., Wang, W.: Saliency-SVM: an automatic approach for image segmentation. Neurocomputing 136, 243–255 (2014)

    Article  Google Scholar 

  12. Guo, M., Zhao, Y., Zhang, C., Chen, Z.: Fast object detection based on selective visual attention. Neurocomputing 144, 184–197 (2014)

    Article  Google Scholar 

  13. Treisman, A., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980)

    Article  Google Scholar 

  14. Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. In: Vaina, L.M. (ed.) Matters of Intelligence, pp. 115–141. Springer, Netherlands (1987)

    Chapter  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  17. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, vol. 19, pp. 545–552 (2007)

    Google Scholar 

  18. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)

    Google Scholar 

  19. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2281 (2012)

    Article  Google Scholar 

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

    Google Scholar 

  21. Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  22. Zhang, Y., Han, J., Guo, L.: Saliency detection by combining spatial and spectral information. Opt. Lett. 38(11), 1987–1989 (2013)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  24. Li, J., Levine, M., An, X., Xu, X., He, H.: Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 996–1010 (2013)

    Article  Google Scholar 

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Acknowledgments

This research was supported by the National Science Foundation of China (61401357), the Shaanxi Provincial Natural Science Foundation of China (2013JM1014, 2014JM1032), the Shaanxi Educational Committee Foundation of China (14JK1797), the Specialized Research Fund of Xianyang Normal University (13XSYK009, 14XSYK005).

Author information

Authors and Affiliations

  1. College of Mathematics and Information Science, Xianyang Normal University, Xianyang, 712000, Shaanxi, China

    Yanbang Zhang

  2. China Electronics Technology Group Corporation, No. 39 Research Institute, Xi’an, 712000, Shaanxi, China

    Guolong Fan

Authors
  1. Yanbang Zhang
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  2. Guolong Fan
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Corresponding author

Correspondence to Yanbang Zhang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Incheon, Korea (Republic of)

    Kyungsook Han

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© 2015 Springer International Publishing Switzerland

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Zhang, Y., Fan, G. (2015). Visual Saliency Detection Based on Color Contrast and Distribution. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_26

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  • DOI: https://doi.org/10.1007/978-3-319-22053-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22052-9

  • Online ISBN: 978-3-319-22053-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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