A dynamic saliency attention model based on local complexity

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• 作者：Longsheng Wei^a ; ^b  ; [Author Vitae] ; Nong Sang^a ; ^{nsang@hust.edu.cn}  ; [Author Vitae] ; Yuehuan Wang^a  ; [Author Vitae] ; Qingqing Zheng^a  ; [Author Vitae]
• 关键词：Dynamic saliency model ; Attentional selection ; Local complexity ; Saliency map
• 刊名：Digital Signal Processing
• 出版年：2012
• 期刊代码：128_10512004
• 类别：cp
• 出版时间：September, 2012
• 卷：22
• 期：5
• 页码：760-767
• 文件大小：2623 K

摘要

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始s model and Shi始s model; when the moving objects do not belong to the static salient regions, our model is better than Ban始s model.