Learning ordinal constraint binary codes for fast similarity search. Issue 3 (May 2022)

• Record Type: Journal Article
• Authors: Zhang, Zheng; Pun, Chi-Man
• Is Part Of: Information processing & management. Volume 59:Issue 3(2022)
• Publication Date: 2022-05
• Subjects: Ordinal graph learning -- Hashing-based image retrieval -- Semantic-preserving hashing -- Similarity comparison -- Compact code learning; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.102919
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Similarity search with hashing has become one of the fundamental research topics in computer vision and multimedia. The current researches on semantic-preserving hashing mainly focus on exploring the semantic similarities between pointwise or pairwise samples in the visual space to generate discriminative hash codes. However, such learning schemes fail to explore the intrinsic latent features embedded in the high-dimensional feature space and they are difficult to capture the underlying topological structure of data, yielding low-quality hash codes for image retrieval. In this paper, we propose an ordinal-preserving latent graph hashing (OLGH) method, which derives the objective hash codes from the latent space and preserves the high-order locally topological structure of data into the learned hash codes. Specifically, we conceive a triplet constrained topology-preserving loss to uncover the ordinal-inferred local features in binary representation learning. By virtue of this, the learning system can implicitly capture the high-order similarities among samples during the feature learning process. Moreover, the well-designed latent subspace learning is built to acquire the noise-free latent features based on the sparse constrained supervised learning. As such, the latent under-explored characteristics of data are fully employed in subspace construction. Furthermore, the latent ordinal graph hashing is formulated by jointly exploiting latent space construction and ordinal graph learning. An efficient optimization algorithm is developed to solve the resulting problem to achieve the optimal solution. Extensive experiments conducted on diverse datasets show the effectiveness and superiority of the proposed method when compared to some advanced learning to hash algorithms for fast image retrieval. The source codes of this paper are available at https://github.com/DarrenZZhang/OLGH . Highlights: We, for the first time, conceptualize the feature-level ordinal-preserving hashing for large-scale image retrieval. A discriminative ordinal-preserving latent graph hashing framework is designed to generate high-quality hash codes. Our method can capture the intrinsic latent features and ordinal-constraint local information into the learned hash codes. Our method jointly exploits latent space construction and ordinal graph learning in discriminative learning.