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SIFT Matching with CNN Evidences for Particular Object Retrieval
Zhang Guixuan1,4; Zeng Zhi1; Zhang Shuwu1; Zhang Yuan2; Wu Wanchun3
Source PublicationNEUROCOMPUTING
2017-05-17
Volume238Issue:238Pages:399-409
SubtypeArticle
AbstractMany object instance retrieval systems are typically based on matching of local features, such as SIFT. However, these local descriptors serve as low-level clues, which are not sufficiently distinctive to prevent false matches. Recently, deep convolutional neural networks (CNN) have shown their promise as a semantic-aware representation for many computer vision tasks. In this paper, we propose a novel approach to employ CNN evidences to improve the SIFT matching accuracy, which plays a critical role in improving the object retrieval performance. To weaken the interference of noise, we extract compact CNN representations from a number of generic object regions. Then a query-adaptive method is proposed to choose appropriate CNN evidence to verify each pre-matched SIFT pair. Two different visual matching verification functions are introduced and evaluated. Moreover, we investigate the suitability of fine-tuning the CNN for our proposed approach. Extensive experiments on benchmark dataSets demonstrate the effectiveness of our method for particular object retrieval. Our results compare favorably to the state-of-the-art methods with acceptable memory usage and query time. (C) 2017 Elsevier B.V. All rights reserved.
KeywordParticular Object Retrieval Bag-of-words Sift Matching Convolutional Neural Networks
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2017.01.081
WOS KeywordNEAREST-NEIGHBOR SEARCH ; IMAGE RETRIEVAL ; SCALE ; SIMILARITY
Indexed BySCI
Language英语
Funding OrganizationNational Science and Technology Supporting Program of China(2015BAH49F01) ; Key Technology R&D Program of Beijing(D161100005216001)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000397372100035
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14307
Collection数字内容技术与服务研究中心_版权智能与文化计算
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Alibaba Grp, Beijing, Peoples R China
3.Chongqing Med Univ, Childrens Hosp, Chongqing, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang Guixuan,Zeng Zhi,Zhang Shuwu,et al. SIFT Matching with CNN Evidences for Particular Object Retrieval[J]. NEUROCOMPUTING,2017,238(238):399-409.
APA Zhang Guixuan,Zeng Zhi,Zhang Shuwu,Zhang Yuan,&Wu Wanchun.(2017).SIFT Matching with CNN Evidences for Particular Object Retrieval.NEUROCOMPUTING,238(238),399-409.
MLA Zhang Guixuan,et al."SIFT Matching with CNN Evidences for Particular Object Retrieval".NEUROCOMPUTING 238.238(2017):399-409.
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