Random VLAD based Deep Hashing for Efficient Image Retrieval

TL;DR

This paper introduces RV-SSDH, a deep hashing method integrating a novel random VLAD layer with neural networks, achieving superior image retrieval performance with low complexity.

Contribution

It proposes a new deep hashing architecture combining random VLAD with neural networks, outperforming existing methods like NetVLAD and SSDH in efficiency and accuracy.

Findings

Outperforms NetVLAD and SSDH baselines in retrieval accuracy

Offers a cost-effective trade-off between performance and complexity

Random VLAD layer achieves high accuracy with low computational cost

Abstract

Image hash algorithms generate compact binary representations that can be quickly matched by Hamming distance, thus become an efficient solution for large-scale image retrieval. This paper proposes RV-SSDH, a deep image hash algorithm that incorporates the classical VLAD (vector of locally aggregated descriptors) architecture into neural networks. Specifically, a novel neural network component is formed by coupling a random VLAD layer with a latent hash layer through a transform layer. This component can be combined with convolutional layers to realize a hash algorithm. We implement RV-SSDH as a point-wise algorithm that can be efficiently trained by minimizing classification error and quantization loss. Comprehensive experiments show this new architecture significantly outperforms baselines such as NetVLAD and SSDH, and offers a cost-effective trade-off in the state-of-the-art. In addition, the proposed random VLAD layer leads to satisfactory accuracy with low complexity, thus shows promising potentials as an alternative to NetVLAD.