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《Acta Electronica Sinica》 2017-11
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The Semantic Salient Region Detection Algorithm Based on the Fully Convolutional Networks

ZHENG Yun-fei;ZHANG Xiong-wei;CAO Tie-yong;SUN Meng;The Army Engineering University of PLA;The Army Artilery and Defense Academy of PLA;The Key Laboratory of Polarization Imaging Detection Technology;  
The existing salient region detection algorithms based on visual stimulus and prior knowledge are difficult to detect some complicated salient regions.The human vision system can distinguish these complicated salient regions because of the rich semantic knowledge in the human visual system.We construct a semantic salient region detection network using the fully convolutional structure.Learning the mapping from the low-level features to the human semantic cognition,our network can extract semantic salient region effectively.Aiming to the defects of the semantic salient region map,we introduce the color information,object boundary information and spatial consistency information to derive accurate superpixellevel foreground and background probability.At last,we fuse the foreground and background probability,semantic information and spatial consistency information to derive the final salient region map.The experiments comparing with the state-ofthe-art 15 algorithms on 6 data sets demonstrate the effectiveness of our algorithm.
【Fund】: 国家自然科学基金(No.61471394);; 国家青年自然科学基金(No.61402519);; 江苏省自然科学基金(No.BK2012510 No.BK20140071 No.BK20140074)
【CateGory Index】: TP391.41
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