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AN EFFICIENT DEEP LEARNING HASHING NEURAL NETWORK FOR MOBILE VISUAL
Heng Qi 1,Liang Liu 2 *
1.Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876;Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876
2.
*Correspondence author
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Funding: none
Opened online: 2 February 2018
Accepted by: none
Citation: Heng Qi,Liang Liu.AN EFFICIENT DEEP LEARNING HASHING NEURAL NETWORK FOR MOBILE VISUAL[OL]. [ 2 February 2018] http://en.paper.edu.cn/en_releasepaper/content/4743206
 
 
Mobile visual search applications are emerging that enable users to sense their surroundings with smart phones. However, because of the particular challenges of mobile visual search, achieving a high recognition bitrate has becomes a consistent target of previous related works. In this paper, we propose a few-parameter, low-latency, and high-accuracy deep hashing approach for constructing binary hash codes for mobile visual search. First, we exploit the architecture of the MobileNet model, which significantly decreases the latency of deep feature extraction by reducing the number of model parameters while maintaining accuracy. Second, we add a hash-like layer into MobileNet to train the model on labeled mobile visual data. Evaluations show that the proposed system can exceed state-of-the-art accuracy performance in terms of the MAP. More importantly, the memory consumption is much less than that of other deep learning models. The proposed method requires only 13 MB of memory for the neural network and achieves a MAP of 97.80% on the mobile location recognition dataset used for testing.
Keywords:Mobile visual search; Supervised hashing; Deep learning
 
 
 

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