Abstract
A deep multi-similarity hashing technique based on multi-label semantics has been proposed in this paper as a means of a new controlled deep hashing system for multi-label image retrieval. Our proposed methodology comprises hash code learning and semantically-aware similarity matrix creation. To create a similarity matrix that considers semantic context, we integrate label-level and semantic-level similarity. Using the higher-order statistics of deep features as inputs, we meticulously craft the multi-similarity loss and quantization error loss for hash code learning, ensuring that the learned binary codes retain their high-ranking similarity. Our suggested strategy is successful, as shown by several testing on CIFAR-10 and NUS-WIDE.
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