logo
banner

Journals & Publications

Journals Publications Papers

Papers

Deep Learning Hashing for Mobile Visual Search
Mar 31, 2017Author:
PrintText Size A A

Title: Deep Learning Hashing for Mobile Visual Search  

Authors: Liu, W; Ma, HD; Qi, H; Zhao, D; Chen, ZN 

Author Full Names: Liu, Wu; Ma, Huadong; Qi, Heng; Zhao, Dong; Chen, Zhineng 

Source: EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, 10.1186/s13640-017-0167-4 FEB 21 2017  

Language: English 

Abstract: The 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. 

ISSN: 1687-5281  

IDS Number: EL9ZB  

Unique ID: WOS:000394977500001 

*Click Here to View Full Record