SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in Images
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 a...
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doaj-70a2b1004ac348119fd5a242112988892021-03-29T21:39:03ZengIEEEIEEE Access2169-35362018-01-016714227143410.1109/ACCESS.2018.28820148539972SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in ImagesZhenguo Gao0https://orcid.org/0000-0003-3115-6959Naeem Ayoub1https://orcid.org/0000-0002-7387-4441Danjie Chen2Bingcai Chen3https://orcid.org/0000-0001-7158-6537Zhimao Lu4School of Computer Science and Technology, Dalian University of Technology, Dalian, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian, ChinaCollege of Civil Engineering, Huaqiao University, Xiamen, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian, ChinaIn 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 S1 and S2. Prior map S1 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 mapS2 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 S1 and S2. 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-theart saliency detection algorithms. Our proposed saliency detection algorithm (SAMM) performs better with higher precision_recall, AUC, F-measure, and minimum mean absolute error values.https://ieeexplore.ieee.org/document/8539972/Saliency detectionimage segmentationartificial intelligenceabsorption Markov modeleye fixation predictionguided filter |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhenguo Gao Naeem Ayoub Danjie Chen Bingcai Chen Zhimao Lu |
spellingShingle |
Zhenguo Gao Naeem Ayoub Danjie Chen Bingcai Chen Zhimao Lu SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in Images IEEE Access Saliency detection image segmentation artificial intelligence absorption Markov model eye fixation prediction guided filter |
author_facet |
Zhenguo Gao Naeem Ayoub Danjie Chen Bingcai Chen Zhimao Lu |
author_sort |
Zhenguo Gao |
title |
SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in Images |
title_short |
SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in Images |
title_full |
SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in Images |
title_fullStr |
SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in Images |
title_full_unstemmed |
SAMM: Surroundedness and Absorption Markov Model Based Visual Saliency Detection in Images |
title_sort |
samm: surroundedness and absorption markov model based visual saliency detection in images |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
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 S1 and S2. Prior map S1 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 mapS2 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 S1 and S2. 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-theart saliency detection algorithms. Our proposed saliency detection algorithm (SAMM) performs better with higher precision_recall, AUC, F-measure, and minimum mean absolute error values. |
topic |
Saliency detection image segmentation artificial intelligence absorption Markov model eye fixation prediction guided filter |
url |
https://ieeexplore.ieee.org/document/8539972/ |
work_keys_str_mv |
AT zhenguogao sammsurroundednessandabsorptionmarkovmodelbasedvisualsaliencydetectioninimages AT naeemayoub sammsurroundednessandabsorptionmarkovmodelbasedvisualsaliencydetectioninimages AT danjiechen sammsurroundednessandabsorptionmarkovmodelbasedvisualsaliencydetectioninimages AT bingcaichen sammsurroundednessandabsorptionmarkovmodelbasedvisualsaliencydetectioninimages AT zhimaolu sammsurroundednessandabsorptionmarkovmodelbasedvisualsaliencydetectioninimages |
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1724192516628021248 |