Multiple Code Hashing for Efficient Image Retrieval

TL;DR

This paper introduces Multiple Code Hashing (MCH), a novel approach that learns multiple hash codes per image using deep reinforcement learning, significantly enhancing hash bucket search efficiency and retrieval performance in large-scale image retrieval tasks.

Contribution

It proposes the first method to learn multiple hash codes per image, improving retrieval accuracy and efficiency in complex scenarios compared to traditional single-code hashing methods.

Findings

MCH outperforms existing hashing methods in retrieval accuracy.

MCH significantly reduces the number of hash buckets visited during search.

Deep reinforcement learning effectively learns multiple hash codes for each image.

Abstract

Due to its low storage cost and fast query speed, hashing has been widely used in large-scale image retrieval tasks. Hash bucket search returns data points within a given Hamming radius to each query, which can enable search at a constant or sub-linear time cost. However, existing hashing methods cannot achieve satisfactory retrieval performance for hash bucket search in complex scenarios, since they learn only one hash code for each image. More specifically, by using one hash code to represent one image, existing methods might fail to put similar image pairs to the buckets with a small Hamming distance to the query when the semantic information of images is complex. As a result, a large number of hash buckets need to be visited for retrieving similar images, based on the learned codes. This will deteriorate the efficiency of hash bucket search. In this paper, we propose a novel hashing framework, called multiple code hashing (MCH), to improve the performance of hash bucket search. The main idea of MCH is to learn multiple hash codes for each image, with each code representing a different region of the image. Furthermore, we propose a deep reinforcement learning algorithm to learn the parameters in MCH. To the best of our knowledge, this is the first work that proposes to learn multiple hash codes for each image in image retrieval. Experiments demonstrate that MCH can achieve a significant improvement in hash bucket search, compared with existing methods that learn only one hash code for each image.