CASIA OpenIR  > 机器人理论与应用团队
Salient object detection based on an efficient End-to-End Saliency Regression Network
Xi, Xuanyang1; Luo, Yongkang1; Wang, Peng1; Qiao, Hong1,2
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-01-05
Volume323Issue:1Pages:265-276
Abstract

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.

KeywordSalient object detection Saliency regression Deep convolutional neural networks Fully convolutional networks
DOI10.1016/j.neucom.2018.10.002
WOS KeywordGRAPH
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000448945600022
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22775
Collection机器人理论与应用团队
Corresponding AuthorLuo, Yongkang
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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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