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Saliency detection via Boolean and foreground in a dynamic Bayesian framework

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Abstract

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.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61231014).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Spectral Imaging and Intelligence Sense, Nanjing University of Science and Technology, Nanjing, 210094, China

    Wei Qi, Jing Han, Yi Zhang & Lianfa Bai

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  1. Wei Qi
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  2. Jing Han
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  3. Yi Zhang
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  4. Lianfa Bai
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Correspondence to Lianfa Bai.

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Qi, W., Han, J., Zhang, Y. et al. Saliency detection via Boolean and foreground in a dynamic Bayesian framework. Vis Comput 33, 209–220 (2017). https://doi.org/10.1007/s00371-015-1176-x

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