Traditional saliency map models are usually based on grayscale images and auxiliary color antagonism channels
not carefully considering the relations of the key saliency elements such as relations among color channels and orientations. To overcome this disadvantage
in this paper
we introduce the complementary color theory into the wavelet designs and propose a holistic color saliency map model based on the Gaussian scale mixture (GSM) of natural scene statistics and its Divisive normalization transformation (DNT) in the wavelet domain. The experimental results show that our model achieves better consistency with the visual attention mechanism of the human eyes
especially in processing colorful scenes.
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references
YU Y, WANG B, ZHANG L. Pulse discrete cosine transform for saliency-based visual attention[C]//Development and Learning, ICDL 2009. IEEE 8th International Conference on. Shanghai, China. IEEE, 2009: 1-6.
BRUCE N, TSOTSOS J. Saliency based on information maximization[C]//Advances in neural information processing systems. New York, 2006: 155-162.
MA Q, ZHANG L. Saliency-based image quality assessment criterion[C]//International Conference on Intelligent Computing. Springer, Berlin, Heidelberg, 2008: 1124-1133.
KOCH C, ULLMAN S. Shifts in selective visual attention: towards the underlying neural circuitry[M]. Springer, Dordrecht, Matters of intelligence, 1987: 115-141.
WALTHER D, KOCH C. Modeling attention to salient proto-objects[J]. Neural networks, 2006, 19(9): 1395-1407. DOI: 1395
WAINWRIGHT M J, SIMONCELLI E P. Scale mixtures of Gaussians and the statistics of natural images[C]//Advances in neural information processing systems. United States, Colorado, 2000: 855-861.
PRIDMORE R W. Complementary colors theory of color vision: Physiology, color mixture, color constancy and color perception[J]. Color Research % Application, 2011, 36(6): 394-412. DOI: 394
TREISMAN A M, GELADE G. A feature-integration theory of attention[J].Cognitive psychology, 1980, 12(1): 97-136. DOI: 97
HOU X, ZHANG L. Saliency detection: A spectral residual approach[C]//Computer Vision and Pattern Recognition, CVPR′07. IEEE Conference on. Minneapolis, Minnesota, USA. IEEE, 2007: 1-8.
PRIDMORE R W. Complementary colors: the structure of wavelength discrimination, uniform hue, spectral sensitivity, saturation, chromatic adaptation, and chromatic induction[J]. Color Research % Application, 2009, 34(3): 233-252. DOI: 233
CHEN Y, LI D, ZHANG J Q. Complementary color wavelet: a novel tool for the color image/video analysis and processing[J]. IEEE Transactions on Circuits and Systems for Video Technology, 20 17, 29(3): 1-3. DOI: 10. 1 109/TCSVT.20 17.2776239.
HOU X, HAREL J, KOCH C. Image signature: Highlighting sparse salient regions[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 34(1): 194-201. DOI: 194
ZHANG L, TONG M H, MARKS T K, et al. SUN: A Bayesian framework for saliency using natural statistics[J]. Journal of vision, 2008, 8(7): 32-32. DOI: 32
HAREL J, KOCH C, PERONA P. Graph-based visual saliency[C]//Advances in neural information processing systems. New York, 2006: 545-552.
LI Q, WANG Z. Reduced-reference image quality assessment using divisive normalization-based image representation[J]. IEEE journal of selected topics in signal processing, 2009, 3(2): 202-211. DOI: 202
ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on pattern analysis and machine intelligence, 1998, 20(11): 1254-1259. DOI: 1254
PORTILLA J, STRELA V, WAINWRIGHT M J, et al. Image denoising using scale mixtures of Gaussians in the wavelet domain[J]. IEEE Transactions on Image processing, 2003, 12(11): 1338-1351. DOI: 1338