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

Awning

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Metallniederschl?ge und Metallf?rbungenowever, mastering discriminative binary codes that perfectly preserve full-pairwise similarities in high-dimensional real-valued features remains a challenging task for ensuring optimal performance. To tackle this challenge, this chapter introduces a novel method, Scalable Supervised Asymmetric Hash

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Metallo-Supramolecular Polymers 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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Hydrozirconation and Its Applications,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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