| A Semi-Supervised Method for Surveillance-Based Visual Location Recognition | |
Liu, Pengcheng ; Yang, Peipei; Wang, Chong; Huang, Kaiqi; Tan, Tieniu
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| Source Publication | IEEE TRANSACTIONS ON CYBERNETICS
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| 2017-11-01 | |
| Volume | 47Issue:11Pages:3719-3732 |
| Subtype | Article |
| Abstract | ; In this paper, we are devoted to solving the problem of crossing surveillance and mobile phone visual location recognition, especially for the case that the query and reference images are captured by mobile phone and surveillance camera, respectively. Besides, we also study the influence of the environmental condition variations on this problem. To explore that problem, we first build a cross-device location recognition dataset, which includes images of 22 locations taken by mobile phones and surveillance cameras under different time and weather conditions. Then based on careful analysis of the problems existing in the data, we specifically design a method which unifies an unsupervised subspace alignment method and the semi-supervised Laplacian support vector machine. Experiments are performed on our dataset. Compared with several related methods, our method shows to be more efficient on the problem of crossing surveillance and mobile phone visual location recognition. Furthermore, the influence of several factors such as feature, time, and weather is studied. |
| Keyword | Cross-device (C-d) Recognition Semi-supervised Learning Visual Localization |
| WOS Headings | Science & Technology ; Technology |
| DOI | 10.1109/TCYB.2016.2578639 |
| WOS Keyword | DOMAIN ADAPTATION ; LOCALIZATION ; REPRESENTATION ; GRAPH |
| Indexed By | SCI |
| Language | 英语 |
| Funding Organization | National Basic Research Program of China(2012CB316302) ; National Natural Science Foundation of China(61135002 ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02050000 ; 61403388) ; XDA06040103) |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS ID | WOS:000413003100020 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/12503 |
| Collection | 智能感知与计算 |
| Corresponding Author | Huang, Kaiqi |
| Affiliation | Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, 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 | Liu, Pengcheng,Yang, Peipei,Wang, Chong,et al. A Semi-Supervised Method for Surveillance-Based Visual Location Recognition[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(11):3719-3732. |
| APA | Liu, Pengcheng,Yang, Peipei,Wang, Chong,Huang, Kaiqi,&Tan, Tieniu.(2017).A Semi-Supervised Method for Surveillance-Based Visual Location Recognition.IEEE TRANSACTIONS ON CYBERNETICS,47(11),3719-3732. |
| MLA | Liu, Pengcheng,et al."A Semi-Supervised Method for Surveillance-Based Visual Location Recognition".IEEE TRANSACTIONS ON CYBERNETICS 47.11(2017):3719-3732. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| 07498648.pdf（5404KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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