Compact Hash Code Learning with Binary Deep Neural Network

TL;DR

This paper introduces a novel deep neural network architecture that directly outputs binary hash codes for image retrieval, overcoming optimization challenges and incorporating properties like independence and balance, with end-to-end training.

Contribution

It proposes a new deep hashing model with a hidden layer directly outputting binary codes, and an end-to-end CNN architecture for joint feature extraction and hashing.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively incorporates independence and balance constraints

Uses alternating optimization and relaxation for training

Abstract

Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challenging due to the binary constraints on the hash codes. In this paper, we propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners. The novelty of our network design is that we constrain one hidden layer to directly output the binary codes. This design has overcome a challenging problem in some previous works: optimizing non-smooth objective functions because of binarization. In addition, we propose to incorporate independence and balance properties in the direct and strict forms into the learning schemes. We also include a similarity preserving property in our objective functions. The resulting optimizations involving these binary, independence, and balance constraints are difficult to solve. To tackle this difficulty, we propose to learn the networks with alternating optimization and careful relaxation. Furthermore, by leveraging the powerful capacity of convolutional neural networks, we propose an end-to-end architecture that jointly learns to extract visual features and produce binary hash codes. Experimental results for the benchmark datasets show that the proposed methods compare favorably or outperform the state of the art.