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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Scalable Supervised Asymmetric Hashing,earns two distinctive hashing functions by minimizing regression loss for semantic label alignment and encoding loss for refined latent features. Notably, instead of utilizing only partial similarity correlations, SSAH directly employs the full-pairwise similarity matrix to prevent information loss

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Ordinal-Preserving Latent Graph Hashing,similarities during the feature learning process. Additionally, well-designed latent subspace learning is incorporated to acquire noise-free latent features based on sparse-constrained supervised learning, fully leveraging the latent under-explored characteristics of data in subspace construction. L

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Semantic-Aware Adversarial Training,criminative and semantic properties jointly. Adversarial examples are generated by maximizing the Hamming distance between hash codes of adversarial samples and mainstay features, validated for efficacy in adversarial attack trials. Notably, this chapter formulates the formalized adversarial trainin

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shing techniques. These approaches can empower readers to proficiently grasp the fundamental principles of the traditional and state-of-the-art methods in binary representations, modeling, and learning. The the978-981-97-2114-6978-981-97-2112-2

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