Paper Info
Title
Deep learning hashing for mobile visual search.
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.
Year
DOI
Venue
2017
10.1186/s13640-017-0167-4
EURASIP J. Image and Video Processing
Keywords
Field
DocType
Mobile visual search, Deep learning hashing, Deep learning optimization, Mobile location recognition
Visual search,Computer science,Artificial intelligence,Deep learning,Mobile phone,Computation,Computer vision,Pattern recognition,Binary code,Exploit,Mobile device,Hash function,Machine learning
Journal
Volume
Issue
ISSN
2017
1
1687-5281
Citations 
PageRank 
References 
13
0.49
58
Authors
5
Name
Order
Citations
PageRank
Wu Liu127534.53
Huadong Ma22020179.93
Heng Qi321830.45
Dong Zhao435429.82
Zhineng Chen519225.29