ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval

TL;DR

ExchNet is a novel end-to-end trainable hashing network that generates compact binary codes for large-scale fine-grained image retrieval, improving speed, storage, and accuracy over existing methods.

Contribution

Proposes a unified attention-based hashing network with local-global feature extraction and feature exchanging for fine-grained image retrieval.

Findings

Outperforms state-of-the-art hashing methods on five datasets.

Achieves superior speed-up and storage reduction compared to other approximate nearest neighbor methods.

Demonstrates effectiveness and practicality through extensive experiments.

Abstract

Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects. In this paper, we study the novel fine-grained hashing topic to generate compact binary codes for fine-grained images, leveraging the search and storage efficiency of hash learning to alleviate the aforementioned problems. Specifically, we propose a unified end-to-end trainable network, termed as ExchNet. Based on attention mechanisms and proposed attention constraints, it can firstly obtain both local and global features to represent object parts and whole fine-grained objects, respectively. Furthermore, to ensure the discriminative ability and semantic meaning's consistency of these part-level features across images, we design a local feature alignment approach by performing a feature exchanging operation. Later, an alternative learning algorithm is employed to optimize the whole ExchNet and then generate the final binary hash codes. Validated by extensive experiments, our proposal consistently outperforms state-of-the-art generic hashing methods on five fine-grained datasets, which shows our effectiveness. Moreover, compared with other approximate nearest neighbor methods, ExchNet achieves the best speed-up and storage reduction, revealing its efficiency and practicality.