Foolad, S., Maleki, A. (2018). Graph-based Visual Saliency Model using Background Color. Journal of AI and Data Mining, 6(1), 145-156. doi: 10.22044/jadm.2017.911
Sh. Foolad; A. Maleki. "Graph-based Visual Saliency Model using Background Color". Journal of AI and Data Mining, 6, 1, 2018, 145-156. doi: 10.22044/jadm.2017.911
Foolad, S., Maleki, A. (2018). 'Graph-based Visual Saliency Model using Background Color', Journal of AI and Data Mining, 6(1), pp. 145-156. doi: 10.22044/jadm.2017.911
Foolad, S., Maleki, A. Graph-based Visual Saliency Model using Background Color. Journal of AI and Data Mining, 2018; 6(1): 145-156. doi: 10.22044/jadm.2017.911
Graph-based Visual Saliency Model using Background Color
1Department of Electrical & Computer Engineering, Semnan University, Semnan, Iran.
2Faculty of Biomedical Engineering, Semnan University, Semnan, Iran.
Abstract
Visual saliency is a cognitive psychology concept that makes some stimuli of a scene stand out relative to their neighbors and attract our attention. Computing visual saliency is a topic of recent interest. Here, we propose a graph-based method for saliency detection, which contains three stages: pre-processing, initial saliency detection and final saliency detection. The initial saliency map is obtained by putting adaptive threshold on color differences relative to the background. In final saliency detection, a graph is constructed, and the ranking technique is exploited. In the proposed method, the background is suppressed effectively, and often salient regions are selected correctly. Experimental results on the MSRA-1000 database demonstrate excellent performance and low computational complexity in comparison with the state-of-the-art methods.
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