Improve Deep Unsupervised Hashing via Structural and Intrinsic Similarity Learning

Abstract

Hashing has attracted increasing attention in image retrieval recently due to its storage and computational efficiency. Although several deep unsupervised hashing methods have been proposed lately, their effectiveness is far from satisfactory in practice owing to two drawbacks. On the one hand, they mostly construct binary similarity matrices which could neglect the confidence differences among multiple similarity signals. On the other hand, they ignore the desired properties of hash codes (i.e., independence and robustness). In this paper, we propose an effective unsupervised hashing method called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> ashing via <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> tructural and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ntrinsic si <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ilarity learning (HashSIM) to tackle these issues in an end-to-end manner. Specifically, HashSIM utilizes both highly and normally confident image pairs to jointly build a continuous similarity matrix, which guides hash code learning via structural similarity learning. Moreover, inspired by contrastive learning, we impose an intrinsic similarity learning objective, which can maximally satisfy the independence and robustness properties of hash bits. Extensive experiments on three popular benchmark datasets demonstrate that our HashSIM outperforms a broad range of state-of-the-art baselines.