SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in Images
| Autor: | Chen Bingcai, Lu Zhimao, Naeem Ayoub, Danjie Chen, Zhenguo Gao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
General Computer Science
Markov process 02 engineering and technology Markov model Salient objects Background noise symbols.namesake 0202 electrical engineering electronic engineering information engineering General Materials Science Cluster analysis image segmentation Mathematics Visual saliency Markov chain Saliency detection eye fixation prediction General Engineering 020206 networking & telecommunications artificial intelligence absorption Markov model Object detection guided filter symbols 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Algorithm lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 6, Pp 71422-71434 (2018) |
| ISSN: | 2169-3536 |
| Popis: | In this paper, we propose a saliency detection method (SAMM) by using the surroundedness and absorption Markov model. First, the approximate area of the salient object is predicted by the surroundedness to the eye fixation point prediction. Second, a simple linear iterative clustering algorithm is applied to the original image to calculate superpixels, and a two-ring image graph model is formed. We calculate two initial saliency maps $S_{1}$ and $S_{2}$ . Prior map $S_{1}$ is calculated by applying the absorption Markov chain, as the superpixel-based region of the two boundaries farthest from the predicted salient object is taken as the background region, while map $S_{2}$ is calculated by using the absorption Markov chain to detect the superpixels in the approximate region of the salient object as a foreground region. The final saliency map is obtained by combining $S_{1}$ and $S_{2}$ . Finally, a guided filter is used to reduce the background noise from the saliency map. For the evaluation, experiments are performed on six publicly available test datasets (MSRA, ECSSD, Imgsal, DUT-OMRON, PASCAL-S, and MSRA10k), and the results are compared against 10 state-of-the-art saliency detection algorithms. Our proposed saliency detection algorithm (SAMM) performs better with higher precision_recall, AUC, ${F}$ -measure, and minimum mean absolute error values. |
| Databáze: | OpenAIRE |
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