吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (01): 177-183.doi: 10.13229/j.cnki.jdxbgxb201401030

• 论文 • 上一篇    下一篇

基于空域和频域的图像显著区域检测

纪超, 刘慧英, 孙景峰, 贺胜, 黄民主   

  1. 西北工业大学 自动化学院, 西安 710072
  • 收稿日期:2013-03-28 出版日期:2014-01-01 发布日期:2014-01-01
  • 作者简介:纪超（1987-），男，博士研究生.研究方向：图像处理与机器视觉.E-mail：dachao9898@163.com
  • 基金资助:

    航空科学基金项目（2012ZC53042）.

Image salient region detection based on spatial and frequency domains

JI Chao, LIU Hui-ying, SUN Jing-feng, HE Sheng, HUANG Min-zhu   

  1. School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
  • Received:2013-03-28 Online:2014-01-01 Published:2014-01-01

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摘要：

根据人眼视觉注意机制，提出将图像在空域中采用局部复杂密度对比和全局颜色分布估计，在频域中通过有效频段分割的方法分别提取显著特征，再仿照细胞调节原理进行特征组合。局部复杂密度对比是模仿万有引力定律，通过稀疏基建模的方式计算视觉注意力的大小；提出在频域内采用有效频段分割，结合幅度信息提取显著特征后加权合成。仿真实验证明本文算法能高效地检测出场景中的显著区域。最后将本文算法应用于虚拟与现实交互中检测真实场景中的有效区，效果良好。

关键词: 计算机应用, 显著特征提取, 稀疏表达, 频段分割, 特征组合

Abstract:

According to the human visual attention mechanism, an image salient region detection method is proposed. The salient features of image are extracted in spatial domain using local clutter density contrast and global color distribution estimation, and extracted in frequency domain using the efficient band divided method. Then, referring to the principle that cells respond to stimuli, such features are combined using the theory of feature combination. Local clutter density contrast imitates the law of universal gravitation, computing the visual attention on sparse matrix model. The efficient band divided method is used in combining information of amplitude for salient feature detection in frequency domain, later utilizes all features with weight. Experiment results show that the algorithm can effectively extract salient regions. The visual attention model is effectively applied to virtual and reality interaction to detect the effective region in real scene.

Key words: computer application, saliency detection, sparse representation, efficient band divided method, feature combination

中图分类号: 

  • TP391.4

[1] 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.

[2] Seoand H, Milanfar P. Non parametric bottom-up saliency detection by self-resemblance[C]//IEEE Conference of Computer Vision and Pattern Recognition, 2009:420-426.

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

[4] Achanta R, Hemami S, Estrada F, et al, Frequency-tuned salient region detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2009:1597-1604.

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

[6] Chen M M, Zhang G X, Mitra N J. Global contrast based salient region detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2011:409-416.

[7] Hou X, Zhang L. Saliency detection: a spectral residual approach[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2007:1-8.

[8] Bruce N, Tsotsos J K. Saliency, attention, and visual search: an information theoretic approach[J]. Journal of Vision, 2009, 9(3):1-24.

[9] Lin Y, Fang B, Yuanyan T. A computational model for saliency maps by using local entropy[C]//Proceedings of Twenty-Fourth AAAI Conference of Artificial Intelligence, 2010:967-973.

[10] Gopalakrishnan V, Hu Y, Deepu R, et al. Salient region detection by modeling distributions of color and orientation[J]. IEEE Transactions on Multimedia, 2011, 2(5):892-905.

[11] Liu Z, Xue Y, Yan H, et al. Efficient saliency detection based on gaussian models[J]. IET Image Processing, 2011, 5(2):122-131.

[12] 申铉京, 于凯民, 王开业, 等. 结合全局和局部灰度拟合活动轮廓模型的图像分割算法[J]. 吉林大学学报:工学版2012, 42(6):1532-1538. Shen Xuan-jing, Yu Kai-min, Wang Kai-ye, et al. Image segmentation algorithm of combining global and local grayscale fitting for active contour model[J]. Journal of Jilin University(Engineering and Technology Edition), 2012, 42(6):1532-1538.

[13] Feng J, Wei Y, Tao L, et al. Salient object detection by composition[C]//IEEE International Conference on Computer Vision, 2011:2113-2120.

[14] 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.
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