A dynamic saliency attention model based on local complexity

Authors:
Longsheng Wei;Nong Sang;Yuehuan Wang;Qingqing Zheng
Affiliations:
Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China and Faculty of Mechanical and Electronic Information, China Unive ...;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China
Venue:
Digital Signal Processing
Year:
2012

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Abstract

A dynamic saliency attention model based on local complexity is proposed in this paper. Low-level visual features are extracted from current and some previous frames. Every feature map is resized into some different sizes. The feature maps in same size and same feature for all the frames are used to calculate a local complexity map. All the local complexity maps are normalized and are fused into a dynamic saliency map. In the same time, a static saliency map is acquired by the current frame. Then dynamic and static saliency maps are fused into a final saliency map. Experimental results indicate that: when there is noise among the frames or there is change of illumination among the frames, our model is excellent to Marat@?s model and Shi@?s model; when the moving objects do not belong to the static salient regions, our model is better than Ban@?s model.