CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Socio-mobile landmark recognition using local features with adaptive region selection
Zhang, Chunjie1; Zhang, Yifan2; Zhu, Xiaobin3; Xue, Zhe1; Qin, Lei4; Huang, Qingming1,4; Tian, Qi5
Source PublicationNEUROCOMPUTING
2016-01-08
Issue172Pages:100-113
SubtypeArticle
AbstractWith the fast development of mobile devices as well as the broadband wireless network, mobile devices are playing a more and more important role in people's daily life. Nowadays, many landmark images are captured by mobile devices. However, these images are often captured under different lightening conditions with varied poses and camera orientations. Besides, people are inherently connected by personal interests as well as various interactions. To alleviate the imaging problem with mobile devices as well as take advantage of the social information for mobile visual applications, we propose a novel socio-mobile visual recognition method using local features with adaptive region selection. We densely extract local regions and use the pixel gradients to represent each local region. Each local region is divided into 4 x 4 subregions to combine the spatial information. Instead of using fixed pixel numbers for each subregion, we adaptively choose the proper size of each subregion to cope with varied poses and camera orientations. The most discriminative local features are then chosen by minimizing the sparse coding loss. Besides, a geo-discriminative codebook is also generated to take advantages of images' location information. Moreover, we jointly consider the visual distances as well as user's friends' matching results to further boost the final visual recognition performance. We achieve the state-of-the-art performance on the Stanford mobile visual search dataset and the San Francisco landmark dataset. These experimental results demonstrate the effectiveness and efficiency of the proposed adaptive region selection based local features for sodo-mobile landmark recognition. (C) 2015 Elsevier B.V. All rights reserved.
KeywordMobile Social Relationship Adaptive Region Selection Local Feature Geo-codebook Visual Recognition
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2014.10.105
WOS KeywordIMAGE CLASSIFICATION ; ANNOTATION ; SCALE
Indexed BySCI
Language英语
Funding OrganizationNational Basic Research Program of China (973 Program)(2012CB316400) ; National Natural Science Foundation of China(61303154 ; 61202325 ; 61402023 ; 61379100 ; 61133003 ; 61332016)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000364884700012
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10519
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Beijing Technol & Business Univ, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intell Info Proc, Beijing 100190, Peoples R China
5.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
Recommended Citation
GB/T 7714
Zhang, Chunjie,Zhang, Yifan,Zhu, Xiaobin,et al. Socio-mobile landmark recognition using local features with adaptive region selection[J]. NEUROCOMPUTING,2016(172):100-113.
APA Zhang, Chunjie.,Zhang, Yifan.,Zhu, Xiaobin.,Xue, Zhe.,Qin, Lei.,...&Tian, Qi.(2016).Socio-mobile landmark recognition using local features with adaptive region selection.NEUROCOMPUTING(172),100-113.
MLA Zhang, Chunjie,et al."Socio-mobile landmark recognition using local features with adaptive region selection".NEUROCOMPUTING .172(2016):100-113.
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