Abstract Center-surround based contrast calculation is rarely applied in deep learning-based algorithms. Therefore, a salient object detection method based on deep center-surround pyramid is proposed. Center-surround based contrast and convolutional neural network are combined for salient object detection. Firstly, deep semantic features are introduced into each stage of the network. Then, the dilated convolution is employed to build the center-surround pyramids to capture the contrast information of different scales and generate the corresponding multi-scale conspicuous maps. Finally, all conspicuous maps are further fused to produce final salient object detection result. Comparative experiments on four public datasets verify that the proposed algorithm achieves lower mean average error and higher F measure.
Fund:National Natural Science Foundation of China(No.61502358,61502357)
Corresponding Authors:
ZHU Lei, Ph.D., associate professor. His research interests include deep learning, salient object detection, object detection and semantic segmentation.
About author:: CHEN Qin, master student. Her research interests include deep learning, salient object detection and semantic segmentation. HOU Yunlong, master student. His research interests include deep learning, salient object detection and semantic segmentation. DENG Huiping, Ph.D., associate profe-ssor. Her research interests include multimedia processing and communication. WU Jin, Ph.D., professor. Her research interests include image processing, pattern recognition, signal processing, multimedia communication, detection technology and automation equipment.
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2020, Vol. 33 
