Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval

TL;DR

This paper introduces a novel deep hashing approach for multi-label image retrieval that redefines pairwise similarity as a percentage based on normalized labels, leading to improved retrieval performance.

Contribution

It proposes a new similarity measure and tailored loss functions for better multi-label image hashing, outperforming existing methods.

Findings

Outperforms state-of-the-art methods on three datasets.

Achieves higher retrieval accuracy in multi-label scenarios.

Effectively captures nuanced similarities among multi-label images.

Abstract

Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning-based methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is '1' if they share no less than one class label and '0' if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the instance similarity is quantified into a percentage based on the normalized semantic labels. Based on the instance similarity, a weighted cross-entropy loss and a minimum mean square error loss are tailored for loss-function construction, and are efficiently used for simultaneous feature learning and hash coding. Experiments on three popular datasets demonstrate that, the proposed method outperforms the competing methods and achieves the state-of-the-art performance in multi-label image retrieval.