Saliency detection via Boolean and foreground in a dynamic Bayesian framework

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• 作者：Wei Qi ; Jing Han ; Yi Zhang ; Lianfa Bai
• 关键词：Propagation mechanism ; Dynamic Bayesian ; Weight matrix
• 刊名：The Visual Computer
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：33
• 期：2
• 页码：209-220
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-2315
• 卷排序：33

文摘

The goal of saliency detection is to locate important regions in an image which attract viewers’ attention the most. In this paper, we propose a dynamic Bayesian model for saliency detection in which both Boolean-based and foreground-based models are exploited. First, a preliminary saliency map is constructed based on multi-channel Boolean maps, and a propagation mechanism is utilized to further modify the saliency map by learning a new weight matrix based on color and spatial structure information. Second, a foreground-based model based on foreground seeds from Boolean-based model is generated to detect salient pixels, and a better result is obtained by applying the edge map and a new weight matrix. Finally, pixel-level saliency is computed using a dynamic Bayesian framework. Both qualitative and quantitative evaluations on several benchmark datasets demonstrate robustness and effectiveness of our approach against state-of-the-art approaches.