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DeepSearch: A Fast Image Search Framework for Mobile Devices

文献类型：期刊论文

作者Wang, Peisong1,2; Hu, Qinghao1,2; Fang, Zhiwei1,2; Zhao, Chaoyang1,2; Cheng, Jian1,2,3
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2018
卷号14期号:1页码:6
关键词Convolutional Neural Networks Acceleration Image Retrieval Tensor Decomposition
DOI10.1145/3152127
文献子类Article
英文摘要Content-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.
WOS关键词HIGHER-ORDER TENSOR ; RETRIEVAL ; DECOMPOSITIONS ; SIMILARITY ; SHAPE
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000425646500006
资助机构National Natural Science Foundation of China(61332016) ; Jiangsu Key Laboratory of Big Data Analysis Technology ; 863 program(2014AA015105)
源URL[http://ir.ia.ac.cn/handle/173211/20896]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
通讯作者Cheng, Jian
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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