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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yan Huang Wei Wang Liang Wang Tieniu Tan |
| Copyright Year | 2012 |
| Description | Author affiliation: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China (Yan Huang; Wei Wang; Liang Wang; Tieniu Tan) |
| Abstract | In this paper, we propose a new region-based saliency model to simulate the human visual attention. First, we construct a pixel-level fully-connected graph representation for an image, and perform normalized cut to segment the image based on the proximity and similarity principles. After obtaining image regions, we reconstruct a region-based fully-connected graph. Based on the saliency principle “center-surround contrast”, we define new dissimilarity functions in terms of several visual features. Finally we run a random walk on the region graph and apply site entropy rate to measure the region saliency. We evaluate the proposed model on a public dataset consisting of 120 images. Experimental results demonstrate that our model predicts eye fixations more accurately than the other four state-of-the-art methods. We also apply our saliency model to improve the performance of image retargeting. |
| Starting Page | 1407 |
| Ending Page | 1410 |
| File Size | 432951 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467322164 |
| ISSN | 10514651 |
| e-ISBN | 9784990644109 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-11-11 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | ICPR Org Committee |
| Subject Keyword | Visualization Entropy Computational modeling Image segmentation Humans Image edge detection Predictive models |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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