Saliency Detection Algorithm Based on Watershed Method and Regional Spatial Attention Model

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Abstract:

This paper presents a novel saliency detection method for color image based on watershed method and regional spatial attention model. Watershed method is improved to divide the original image into several sub-regions. And regional spatial attention model is specifically designed in the terms of visual attention mechanism from human's psychology and physiology. Experiment results show that our method can accurately and quickly detect the salient regions from color images, and compared with traditional ones, our method performs better.

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Advanced Materials Research (Volumes 753-755)

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3047-3050

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Koch C, Poggio T. Predicting the visual world: silence is golden[J], Nature Neuroscience, 1999, 2: 9-10.

Google Scholar

[2] Koffka K. Principles of Gestalt Psychology: Routledge & Kegan Paul; (1955).

Google Scholar

[3] Treisman A, Gelade G. A feature-integration theory of attention[J], Cognitive Psychology, 1980, 12(1): 97-136.

DOI: 10.1016/0010-0285(80)90005-5

Google Scholar

[4] Wolfe J. A revised model of visual search[J], Psychonomic bulletin & review, 1994, 1(2): 202-38.

Google Scholar

[5] Goferman S, Zelnik-Manor L, Tal A. Context-aware saliency detection. 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. pp.2376-83.

DOI: 10.1109/cvpr.2010.5539929

Google Scholar

[6] Zhang P, Wang RS. A survey of detection region of interestin a static image. Journal of Image and Graphics [J], 2005, 10(2): 142-8.

Google Scholar

[7] Itti L, Koch C, Niebur EA. Model of saliency—based visual attention for rapid scene analysis[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-9.

DOI: 10.1109/34.730558

Google Scholar

[8] Itti L, Koch C. Computational modelling of visual attention[J], Nature Reviews Neuroscience, 2001, 2(3): 194-203.

DOI: 10.1038/35058500

Google Scholar

[9] Bruce N, Tsotsos J. Saliency based on information maximization[J], Advances in Neural Information Processing Systems, 2006, 18(155).

Google Scholar

[10] Harel J, Koch C, Perona P. Graph-based visual saliency[J], Advances in Neural Information Processing Systems, 2007, 19(545).

Google Scholar

[11] Guo C, Ma Q, Zhang L. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. IEEE Conference on Computer Vision and Pattern Recognition, 2008. pp.1-8.

DOI: 10.1109/cvpr.2008.4587715

Google Scholar

[12] Hou X, Zhang L. Saliency detection: a spectral residual approach. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007. pp.1-8.

DOI: 10.1109/cvpr.2007.383267

Google Scholar

[13] Achanta R, Hemami S, Estrada F. Frequency-tuned salient region detection. International Conference on Computer Vision and Pattern Recognition (CVPR), 2009. pp.1597-604.

DOI: 10.1109/cvpr.2009.5206596

Google Scholar