Titlebook: Binary Representation Learning on Visual Images; Learning to Hash for Zheng Zhang Book 2024 The Editor(s) (if applicable) and The Author(s)

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Probability Ordinal-Preserving Semantic Hashing,ten neglecting the mutual triplet-level ordinal structure crucial for similarity preservation. This chapter introduces a groundbreaking approach–the Probability Ordinal-preserving Semantic Hashing (POSH) framework–pioneering ordinal-preserving hashing under a non-parametric Bayesian theory. The fram

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Ordinal-Preserving Latent Graph Hashing, samples in the visual space to generate discriminative hash codes. However, these approaches neglect the intrinsic latent features within the high-dimensional feature space, making it challenging to capture the underlying topological structure of data and resulting in suboptimal hash codes for imag

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Deep Collaborative Graph Hashing,and the computational efficiency of compact hash code learning. However, existing deep semantic-preserving hashing approaches often treat semantic labels as ground truth for classification or transform them into prevalent pairwise similarities, overlooking interactive correlations between visual sem

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978-981-97-2114-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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Zheng ZhangBroadens the understanding of binary representation learning in the context of visual data.Offers the latest research trends in binary representation, modeling and learning.Expounds the potential, int

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