CASIA OpenIR
Unsupervised Semantic-Based Aggregation of Deep Convolutional Features
Xu, Jian1,2; Wang, Chunheng2; Qi, Chengzuo1,2; Shi, Cunzhao2; Xiao, Baihua2
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-02-01
Volume28Issue:2Pages:601-611
Corresponding AuthorWang, Chunheng(chunheng.wang@ia.ac.cn)
AbstractIn this paper, we propose a simple but effective semantic-based aggregation (SBA) method. The proposed SBA utilizes the discriminative filters of deep convolutional layers as semantic detectors. Moreover, we propose the effective unsupervised strategy to select some semantic detectors to generate the "soft region proposals," which highlight certain discriminative pattern of objects and suppress the noise of background. The final global SBA representation could then be acquired by aggregating the regional representations weighted by the selected " soft region proposals" corresponding to various semantic content. Our unsupervised SBA is easy to generalize and achieves excellent performance on various tasks. We conduct comprehensive experiments and show that our unsupervised SBA outperforms the state-of-the-art unsupervised and supervised aggregation methods on image retrieval, place recognition, and cloud classification.
KeywordUnsupervised semantic-based aggregation semantic detectors
DOI10.1109/TIP.2018.2867104
WOS KeywordIMAGE RETRIEVAL ; SCALE ; CLASSIFICATION ; RECOGNITION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61531019] ; National Natural Science Foundation of China[61601462] ; National Natural Science Foundation of China[71621002]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000446255300006
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25733
Collection中国科学院自动化研究所
Corresponding AuthorWang, Chunheng
Affiliation1.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, 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
Xu, Jian,Wang, Chunheng,Qi, Chengzuo,et al. Unsupervised Semantic-Based Aggregation of Deep Convolutional Features[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(2):601-611.
APA Xu, Jian,Wang, Chunheng,Qi, Chengzuo,Shi, Cunzhao,&Xiao, Baihua.(2019).Unsupervised Semantic-Based Aggregation of Deep Convolutional Features.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(2),601-611.
MLA Xu, Jian,et al."Unsupervised Semantic-Based Aggregation of Deep Convolutional Features".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.2(2019):601-611.
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