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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaoshuai Sun Hongxun Yao Rongrong Ji Pengfei Xu Xianming Liu Shaohui Liu |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Computer Science and Engineering, Harbin Institute of Technology, China (Xiaoshuai Sun; Hongxun Yao; Rongrong Ji; Pengfei Xu; Xianming Liu; Shaohui Liu) |
| Abstract | Representation and measurement are two important issues for saliency models. Different with previous works that learnt sparse features from large scale natural statistics, we propose to learn features from short-term statistics of single images. For saliency measurement, we define background firing rate (BFR) for each sparse feature, and then we propose to use feature activation rate (FAR) to measure the bottom-up visual saliency. The proposed FAR measure is biological plausible and easy to compute, also with satisfied performance. Experiments on human eye fixations and psychological patterns demonstrate the effectiveness and robustness of our proposed method. |
| Starting Page | 1101 |
| Ending Page | 1104 |
| File Size | 751000 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424479924 |
| ISSN | 15224880 |
| e-ISBN | 9781424479948 |
| e-ISBN | 9781424479931 |
| DOI | 10.1109/ICIP.2010.5653713 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-09-26 |
| Publisher Place | Hong Kong |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Visualization Feature extraction Psychology Energy measurement Computational modeling Humans Neurons feature activation rate Saliency detection sparse feature |
| Content Type | Text |
| Resource Type | Article |
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