DeepSearch: A Fast Image Search Framework for Mobile Devices

doi:10.1145/3152127

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 图像与视频分析团队

	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
刊名	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
	2018
卷号	14 期号:1 页码:6
关键词	Convolutional Neural Networks Acceleration Image Retrieval Tensor Decomposition
DOI	10.1145/3152127
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)
内容类型	期刊论文
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.