A survey on deep hashing for image retrieval

TL;DR

This paper surveys deep hashing techniques for image retrieval, evaluates existing methods, introduces a novel Shadow Recurrent Hashing (SRH) approach, and demonstrates its effectiveness on CIFAR-10.

Contribution

It provides a comprehensive review of deep supervised hashing methods and proposes a new SRH method with a unique shadow concept to improve retrieval performance.

Findings

SRH achieves satisfying performance on CIFAR-10

Deep supervised hashing methods are categorized into three main directions

The shadow concept enhances the optimization of CNN outputs

Abstract

Hashing has been widely used in approximate nearest search for large-scale database retrieval for its computation and storage efficiency. Deep hashing, which devises convolutional neural network architecture to exploit and extract the semantic information or feature of images, has received increasing attention recently. In this survey, several deep supervised hashing methods for image retrieval are evaluated and I conclude three main different directions for deep supervised hashing methods. Several comments are made at the end. Moreover, to break through the bottleneck of the existing hashing methods, I propose a Shadow Recurrent Hashing(SRH) method as a try. Specifically, I devise a CNN architecture to extract the semantic features of images and design a loss function to encourage similar images projected close. To this end, I propose a concept: shadow of the CNN output. During optimization process, the CNN output and its shadow are guiding each other so as to achieve the optimal solution as much as possible. Several experiments on dataset CIFAR-10 show the satisfying performance of SRH.