Image structure-based saliency detection

Jing Hong; Yufei Chen; Xianhui Liu; Weidong Zhao; Ning Jia; Qiangqiang Zhou

doi:10.1117/1.JEI.26.4.043019

21 August 2017 Image structure-based saliency detection

Jing Hong, Yufei Chen, Xianhui Liu, Weidong Zhao, Ning Jia, Qiangqiang Zhou

Author Affiliations +

Funded by: National Key Technology Support Program of China, Shanghai Municipal Science Technology Commission’s project, Fundamental Research Funds for the Central Universities

Journal of Electronic Imaging, Vol. 26, Issue 4, 043019 (August 2017). https://doi.org/10.1117/1.JEI.26.4.043019

Abstract

We consider image intrinsic structure in two aspects: foreground saliency computation and background saliency calculation. On the one hand, a segmented image is represented as a weighted undirected graph, and we calculate the foreground saliency by choosing some superpixels as ranking queries, where we apply a robust background measure to select those for accuracy. On the other hand, we compute the posterior probabilities to measure the background saliency and in terms of the probability, we construct a probability tree via multimerging on superpixels, and then apply the optimization strategy to the background saliency. We evaluate our proposed algorithm on two benchmark datasets and our algorithm yields the competitive results when compared with nine state-of-the-art algorithms in terms of five evaluation metrics.

Citation Download Citation

Jing Hong, Yufei Chen, Xianhui Liu, Weidong Zhao, Ning Jia, and Qiangqiang Zhou "Image structure-based saliency detection," Journal of Electronic Imaging 26(4), 043019 (21 August 2017). https://doi.org/10.1117/1.JEI.26.4.043019

Received: 2 May 2017; Accepted: 28 July 2017; Published: 21 August 2017

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