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Unsupervised Semantic-Based Aggregation of Deep Convolutional Features | |
Xu, Jian1,2![]() ![]() ![]() ![]() ![]() | |
Source Publication | IEEE TRANSACTIONS ON IMAGE PROCESSING
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ISSN | 1057-7149 |
2019-02-01 | |
Volume | 28Issue:2Pages:601-611 |
Abstract | In 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. |
Keyword | Unsupervised semantic-based aggregation semantic detectors |
DOI | 10.1109/TIP.2018.2867104 |
WOS Keyword | IMAGE RETRIEVAL ; SCALE ; CLASSIFICATION ; RECOGNITION |
Indexed By | SCI |
Language | 英语 |
Funding Project | National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[61601462] ; National Natural Science Foundation of China[61531019] ; National Natural Science Foundation of China[61531019] ; National Natural Science Foundation of China[61601462] ; National Natural Science Foundation of China[71621002] |
WOS Research Area | Computer Science ; Engineering |
WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS ID | WOS:000446255300006 |
Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/25733 |
Collection | 中国科学院自动化研究所 |
Corresponding Author | Wang, Chunheng |
Affiliation | 1.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 Affilication | Institute of Automation, Chinese Academy of Sciences |
Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
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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SBA.pdf(1963KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download |
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