Salient object detection based on an efficient End-to-End Saliency Regression Network | |
Xi, Xuanyang1; Luo, Yongkang1; Wang, Peng1; Qiao, Hong1,2 | |
刊名 | NEUROCOMPUTING |
2019-01-05 | |
卷号 | 323期号:1页码:265-276 |
关键词 | Salient object detection Saliency regression Deep convolutional neural networks Fully convolutional networks |
ISSN号 | 0925-2312 |
DOI | 10.1016/j.neucom.2018.10.002 |
英文摘要 | Salient object detection aims at detecting and segmenting the most salient objects from images or videos. It serves as a pre-processing step for a variety of computer vision and image processing tasks. Therefore, efficient and simple detection procedure is the primary requirement of salient object detection. Although many methods with impressive performances have been proposed, they always include complicated procedures. They are time-consuming and not easy to be applied in practical application. In order to address this issue, we propose an efficient and simple salient object detection architecture based on saliency regression network. Our method is a simplified end-to-end deep neural network without any pre-processing and post-processing. It can directly predict a dense full-resolution saliency map for a given image with a compact pipeline. Experimental results on five benchmark datasets show that the proposed method can achieve comparable or better precision performance than the state-of-the-art methods while get an improvement in the detection speed. (C) 2018 Elsevier B.V. All rights reserved. |
资助项目 | National Natural Science Foundation of China[91748131] ; National Natural Science Foundation of China[U1613213] ; National Natural Science Foundation of China[61210009] ; National Natural Science Foundation of China[61602483] ; National Natural Science Foundation of China[61771471] ; National Natural Science Foundation of China[61702516] ; National Natural Science Foundation of China[61603389] ; National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; Youth Innovation Promotion Association of CAS[2015112] |
WOS关键词 | GRAPH |
WOS研究方向 | Computer Science |
语种 | 英语 |
出版者 | ELSEVIER SCIENCE BV |
WOS记录号 | WOS:000448945600022 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/22774] |
专题 | 机器人理论与应用团队 |
通讯作者 | Luo, Yongkang |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China |
推荐引用方式 GB/T 7714 | Xi, Xuanyang,Luo, Yongkang,Wang, Peng,et al. Salient object detection based on an efficient End-to-End Saliency Regression Network[J]. NEUROCOMPUTING,2019,323(1):265-276. |
APA | Xi, Xuanyang,Luo, Yongkang,Wang, Peng,&Qiao, Hong.(2019).Salient object detection based on an efficient End-to-End Saliency Regression Network.NEUROCOMPUTING,323(1),265-276. |
MLA | Xi, Xuanyang,et al."Salient object detection based on an efficient End-to-End Saliency Regression Network".NEUROCOMPUTING 323.1(2019):265-276. |
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