CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
DeepSearch: A Fast Image Search Framework for Mobile Devices
Wang, Peisong1,2; Hu, Qinghao1,2; Fang, Zhiwei1,2; Zhao, Chaoyang1,2; Cheng, Jian1,2,3
Source PublicationACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
2018
Volume14Issue:1Pages:6
SubtypeArticle
AbstractContent-based image retrieval (CBIR) is one of the most important applications of computer vision. In recent years, there have been many important advances in the development of CBIR systems, especially Convolutional Neural Networks (CNNs) and other deep-learning techniques. On the other hand, current CNN-based CBIR systems suffer from high computational complexity of CNNs. This problem becomes more severe as mobile applications become more and more popular. The current practice is to deploy the entire CBIR systems on the server side while the client side only serves as an image provider. This architecture can increase the computational burden on the server side, which needs to process thousands of requests per second. Moreover, sending images have the potential of personal information leakage. As the need of mobile search expands, concerns about privacy are growing. In this article, we propose a fast image search framework, named DeepSearch, which makes complex image search based on CNNs feasible on mobile phones. To implement the huge computation of CNN models, we present a tensor Block Term Decomposition (BTD) approach as well as a nonlinear response reconstruction method to accelerate the CNNs involving in object detection and feature extraction. The extensive experiments on the ImageNet dataset and Alibaba Large-scale Image Search Challenge dataset show that the proposed accelerating approach BTD can significantly speed up the CNN models and further makes CNN-based image search practical on common smart phones.
KeywordConvolutional Neural Networks Acceleration Image Retrieval Tensor Decomposition
WOS HeadingsScience & Technology ; Technology
DOI10.1145/3152127
WOS KeywordHIGHER-ORDER TENSOR ; RETRIEVAL ; DECOMPOSITIONS ; SIMILARITY ; SHAPE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61332016) ; Jiangsu Key Laboratory of Big Data Analysis Technology ; 863 program(2014AA015105)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS IDWOS:000425646500006
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20896
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorCheng, Jian
Affiliation1.Chinese Acad Sci, Inst Automat, 95 East Zhongguancun Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Peisong,Hu, Qinghao,Fang, Zhiwei,et al. DeepSearch: A Fast Image Search Framework for Mobile Devices[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2018,14(1):6.
APA Wang, Peisong,Hu, Qinghao,Fang, Zhiwei,Zhao, Chaoyang,&Cheng, Jian.(2018).DeepSearch: A Fast Image Search Framework for Mobile Devices.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,14(1),6.
MLA Wang, Peisong,et al."DeepSearch: A Fast Image Search Framework for Mobile Devices".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 14.1(2018):6.
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