| Visual saliency detection using information divergence | |
Hou, Weilong1; Gao, Xinbo1; Tao, Dacheng2,3 ; Li, Xuelong4
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| 2013-10-01 | |
| 发表期刊 | PATTERN RECOGNITION
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| 卷号 | 46期号:10页码:2658-2669 |
| 摘要 | The technique of visual saliency detection supports video surveillance systems by reducing redundant information and highlighting the critical, visually important regions. It follows that information about the image might be of great importance in depicting the visual saliency. However, the majority of existing methods extract contrast-like features without considering the contribution of information content. Based on the hypothesis that information divergence leads to visual saliency, a two-stage framework for saliency detection, namely information divergence model (IDM), is introduced in this paper. The term "information divergence" is used to express the non-uniform distribution of the visual information in an image. The first stage is constructed to extract sparse features by employing independent component analysis (ICA) and difference of Gaussians (DOG) filter. The second stage improves the Bayesian surprise model to compute information divergence across an image. A visual saliency map is finally obtained from the information divergence. Experiments are conducted on nature image databases, psychological patterns and video surveillance sequences. The results show the effectiveness of the proposed method by comparing it with 13 state-of-the-art visual saliency detection methods. (C) 2013 Elsevier Ltd. All rights reserved. |
| 文章类型 | Article |
| 关键词 | Visual Attention Saliency Detection Independent Component Analysis Bayesian Surprise Model |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.patcog.2013.03.008 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | SPARSE REPRESENTATION ; NEUROBIOLOGICAL MODEL ; IMAGE RETRIEVAL ; NATURAL SCENES ; SIMPLE CELLS ; ATTENTION ; FEATURES ; FILTERS ; OVERT ; RECOGNITION |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000320477400005 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/23444 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 3.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hou, Weilong,Gao, Xinbo,Tao, Dacheng,et al. Visual saliency detection using information divergence[J]. PATTERN RECOGNITION,2013,46(10):2658-2669. |
| APA | Hou, Weilong,Gao, Xinbo,Tao, Dacheng,&Li, Xuelong.(2013).Visual saliency detection using information divergence.PATTERN RECOGNITION,46(10),2658-2669. |
| MLA | Hou, Weilong,et al."Visual saliency detection using information divergence".PATTERN RECOGNITION 46.10(2013):2658-2669. |
| 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 | ||
| Visual saliency dete（8294KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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