| Spatiochromatic Context Modeling for Color Saliency Analysis | |
Zhang, Jun1; Wang, Meng1; Zhang, Shengping2; Li, Xuelong3 ; Wu, Xindong1,4
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| 作者部门 | 光学影像学习与分析中心 |
| 2016-06 | |
| 发表期刊 | IEEE Transactions on Neural Networks and Learning Systems
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| ISSN | 2162237X |
| 卷号 | 27期号:6页码:1177-1189 |
| 产权排序 | 3 |
| 摘要 | Visual saliency is one of the most noteworthy perceptual abilities of human vision. Recent progress in cognitive psychology suggests that: 1) visual saliency analysis is mainly completed by the bottom;up mechanism consisting of feedforward low;level processing in primary visual cortex (area V1) and 2) color interacts with spatial cues and is influenced by the neighborhood context, and thus it plays an important role in a visual saliency analysis. From a computational perspective, the most existing saliency modeling approaches exploit multiple independent visual cues, irrespective of their interactions (or are not computed explicitly), and ignore contextual influences induced by neighboring colors. In addition, the use of color is often underestimated in the visual saliency analysis. In this paper, we propose a simple yet effective color saliency model that considers color as the only visual cue and mimics the color processing in V1. Our approach uses region;/boundary;defined color features with spatiochromatic filtering by considering local color;orientation interactions, therefore captures homogeneous color elements, subtle textures within the object and the overall salient object from the color image. To account for color contextual influences, we present a divisive normalization method for chromatic stimuli through the pooling of contrary/complementary color units. We further define a color perceptual metric over the entire scene to produce saliency maps for color regions and color boundaries individually. These maps are finally globally integrated into a one single saliency map. The final saliency map is produced by Gaussian blurring for robustness. We evaluate the proposed method on both synthetic stimuli and several benchmark saliency data sets from the visual saliency analysis to salient object detection. The experimental results demonstrate that the use of color as a unique visual cue achieves competitive results on par with or better than 12 state;of;the;art approaches. © 2015 IEEE. |
| 关键词 | Image Segmentation Object Recognition Visualization |
| DOI | 10.1109/TNNLS.2015.2464316 |
| 收录类别 | EI |
| 语种 | 英语 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/28251 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.School of Computer Science and Information Engineering, Hefei University of Technology, Hefei; 230009, China 2.School of Computer Science and Technology, Harbin Institute of Technology, Weihai; 264209, China 3.State Key Laboratory of Transient Optics and Photonics, Center for Optical Imagery Analysis and Learning, Xi'An Institute of Optics and Precision Mechanics, Xi'an; 710119, China 4.Department of Computer Science, University of Vermont, Burlington; VT; 05405, United States |
| 推荐引用方式 GB/T 7714 | Zhang, Jun,Wang, Meng,Zhang, Shengping,et al. Spatiochromatic Context Modeling for Color Saliency Analysis[J]. IEEE Transactions on Neural Networks and Learning Systems,2016,27(6):1177-1189. |
| APA | Zhang, Jun,Wang, Meng,Zhang, Shengping,Li, Xuelong,&Wu, Xindong.(2016).Spatiochromatic Context Modeling for Color Saliency Analysis.IEEE Transactions on Neural Networks and Learning Systems,27(6),1177-1189. |
| MLA | Zhang, Jun,et al."Spatiochromatic Context Modeling for Color Saliency Analysis".IEEE Transactions on Neural Networks and Learning Systems 27.6(2016):1177-1189. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Spatiochromatic Cont（8770KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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