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Salient object detection via incorporating multiple manifold ranking

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Abstract

We propose a salient object detection method in this paper via incorporating multifeature-based boundary ranking and boundary connectivity ranking. For multifeature-based boundary ranking, Lab and Lab histogram features are chosen to construct the graph and the regions along each boundary are, respectively, ranked to other regions based on the newly defined graph to get four boundary-based saliency maps. Then, they are integrated to acquire the multifeature-based boundary ranking map. For multifeature-based boundary connectivity ranking, the graph is designed by Lab and spatial distances. The multifeature-based boundary connectivity ranking map is obtained by ranking the boundary connectivity based on the graph, where the boundary connectivity is averaged by adjacent regions and weighted by center prior. The saliency result is obtained by incorporating these two multifeature-based saliency maps with boundary connectivity. Compared with 14 existing models, the method in this paper can obtain preferable results on 3 frequently used datasets.

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  1. School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi’an, 710072, China

    Yanzhao Wang & Guohua Peng

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  1. Yanzhao Wang
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  2. Guohua Peng
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Correspondence to Yanzhao Wang.

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Wang, Y., Peng, G. Salient object detection via incorporating multiple manifold ranking. SIViP 13, 1603–1610 (2019). https://doi.org/10.1007/s11760-019-01507-3

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