An efficient deep learning hashing neural network for mobile visual search

TL;DR

This paper introduces a low-latency, high-accuracy deep hashing neural network based on MobileNet for mobile visual search, achieving state-of-the-art performance with minimal memory usage.

Contribution

It presents a novel deep hashing approach using MobileNet architecture with a hash-like layer, optimized for mobile visual search with reduced parameters and high accuracy.

Findings

Achieves 97.80% MAP on mobile location recognition dataset.

Requires only 13 MB of memory for the neural network.

Outperforms state-of-the-art accuracy in mobile visual search.

Abstract

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.