互补色小波域自然场景统计显著图模型
|
摘要
传统显著图(Saliency map)通常基于灰度图像+彩色拮抗辅助通道的模型。其未能整体充分考虑颜色通道之间、颜色与方向等显著性要素之间关系.为了克服这样的缺点,本文将人眼视觉有重要作用的互补色理论引入小波设计,提出一种基于自然场景的高斯尺度混合模型(Gaussian scale mixture, GSM)及其分区归一化变换(Divisive normalization transformation, DNT)的彩色整体显著图模型.实验结果表明,该模型较其他同类模型有显著优越性,特别在处理色彩丰富的场景时能大幅提高与人眼视觉机制一致性.
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.
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.
引文
[1] TREISMAN A M, GELADE G. A feature-integration theory of attention[J].Cognitive psychology, 1980, 12(1): 97-136.
[2] KOCH C, ULLMAN S. Shifts in selective visual attention: towards the underlying neural circuitry[M]. Springer, Dordrecht, Matters of intelligence, 1987: 115-141.
[3] 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.
[4] WALTHER D, KOCH C. Modeling attention to salient proto-objects[J]. Neural networks, 2006, 19(9): 1395-1407.
[5] HAREL J, KOCH C, PERONA P. Graph-based visual saliency[C]//Advances in neural information processing systems. New York, 2006: 545-552.
[6] BRUCE N, TSOTSOS J. Saliency based on information maximization[C]//Advances in neural information processing systems. New York, 2006: 155-162.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 黄虹, 张建秋. 彩色自然场景统计显著图模型[J]. 复旦学报: 自然科学版, 2014, 59(1): 51-58.
[15] 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, 2017,29(3):1-3. DOI: 10.1109/TCSVT.2017.2776239.
[16] MA Q, ZHANG L. Saliency-based image quality assessment criterion[C]//International Conference on Intelligent Computing. Springer, Berlin, Heidelberg, 2008: 1124-1133.
[17] 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.
[18] 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.
[2] KOCH C, ULLMAN S. Shifts in selective visual attention: towards the underlying neural circuitry[M]. Springer, Dordrecht, Matters of intelligence, 1987: 115-141.
[3] 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.
[4] WALTHER D, KOCH C. Modeling attention to salient proto-objects[J]. Neural networks, 2006, 19(9): 1395-1407.
[5] HAREL J, KOCH C, PERONA P. Graph-based visual saliency[C]//Advances in neural information processing systems. New York, 2006: 545-552.
[6] BRUCE N, TSOTSOS J. Saliency based on information maximization[C]//Advances in neural information processing systems. New York, 2006: 155-162.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 黄虹, 张建秋. 彩色自然场景统计显著图模型[J]. 复旦学报: 自然科学版, 2014, 59(1): 51-58.
[15] 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, 2017,29(3):1-3. DOI: 10.1109/TCSVT.2017.2776239.
[16] MA Q, ZHANG L. Saliency-based image quality assessment criterion[C]//International Conference on Intelligent Computing. Springer, Berlin, Heidelberg, 2008: 1124-1133.
[17] 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.
[18] 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.