21 August 2017 Image structure-based saliency detection
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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.
© 2017 SPIE and IS&T
Jing Hong, Jing Hong, Yufei Chen, Yufei Chen, Xianhui Liu, Xianhui Liu, Weidong Zhao, Weidong Zhao, Ning Jia, Ning Jia, Qiangqiang Zhou, 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 . Submission: Received: 2 May 2017; Accepted: 28 July 2017
Received: 2 May 2017; Accepted: 28 July 2017; Published: 21 August 2017

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