CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Deep learning hashing for mobile visual search
Liu, Wu1; Ma, Huadong1; Qi, Heng1; Zhao, Dong1; Chen, Zhineng2
Source PublicationEURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
2017-02-21
SubtypeReview
AbstractThe proliferation of mobile devices is producing a new wave of mobile visual search applications that enable users to sense their surroundings with smart phones. As the particular challenges of mobile visual search, achieving high recognition bitrate becomes the consistent target of existed related works. In this paper, we explore to holistically exploit the deep learning-based hashing methods for more robust and instant mobile visual search. Firstly, we present a comprehensive survey of the existed deep learning based hashing methods, which showcases their remarkable power of automatic learning highly robust and compact binary code representation for visual search. Furthermore, in order to implement the deep learning hashing on computation and memory constrained mobile device, we investigate the deep learning optimization works to accelerate the computation and reduce the model size. Finally, we demonstrate a case study of deep learning hashing based mobile visual search system. The evaluations show that the proposed system can significantly improve 70% accuracy in MAP than traditional methods, and only needs less than one second computation time on the ordinary mobile phone. Finally, with the comprehensive study, we discuss the open issues and future research directions of deep learning hashing for mobile visual search.
WOS HeadingsScience & Technology ; Technology
DOI10.1186/s13640-017-0167-4
WOS KeywordIMAGE RETRIEVAL ; SPACE
Indexed BySCI
Language英语
Funding Organizationnational key research and development plan(2016YFC0801005) ; National Natural Science Foundation of China(61602049 ; Funds for Creative Research Groups of China(61421061) ; Beijing Training Project for the Leading Talents in ST(ljrc 201502) ; CCF-Tencent Open Research Fund(AGR20160113) ; 61332005)
WOS Research AreaEngineering ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS IDWOS:000394977500001
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14396
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation1.Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Liu, Wu,Ma, Huadong,Qi, Heng,et al. Deep learning hashing for mobile visual search[J]. EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING,2017.
APA Liu, Wu,Ma, Huadong,Qi, Heng,Zhao, Dong,&Chen, Zhineng.(2017).Deep learning hashing for mobile visual search.EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING.
MLA Liu, Wu,et al."Deep learning hashing for mobile visual search".EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING (2017).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu, Wu]'s Articles
[Ma, Huadong]'s Articles
[Qi, Heng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Wu]'s Articles
[Ma, Huadong]'s Articles
[Qi, Heng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Wu]'s Articles
[Ma, Huadong]'s Articles
[Qi, Heng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.