Salient Object Detection Based on Deep Center-Surround Pyramid

被引：0
|
作者：
Chen Q. [1 ]
Zhu L. [1 ]
Hou Y. [1 ]
Deng H. [1 ]
Wu J. [1 ]
机构：
[1] School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan
来源：
Zhu, Lei (zhulei@wust.edu.cn) | 1600年 / Science Press卷 / 33期
基金：
中国国家自然科学基金;
关键词：
Center-Surround Contrast; Convolutional Neural Networks; Dilated Convolution; Salient Object Detection;
D O I：
10.16451/j.cnki.issn1003-6059.202006003
中图分类号：
学科分类号：
摘要：
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. © 2020, Science Press. All right reserved.
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收藏
页码：496 / 506
页数：10
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