Label-Semantic-Enhanced Online Hashing for Efficient Cross-modal Retrieval
Abstract: Existing online cross-modal hashing methods often treat the semantic label categories independently to correlate the semantically similar data instances, which intrinsically ignore the potential dependency between the label categories and thus fail to capture the discriminative information in the hash code learning process. To alleviate this concern, we explore the inter-dependency between the label categories through their co-occurrence correlation from the label set, and present an efficient Label-Semantic-Enhanced Online Hashing (LSE-OH) method for various cross-modal retrieval task. To be specific, the proposed framework integrates the instance-wise similarity and label-category affinity to incrementally learn the discriminative hash codes for the current arriving data, while updating the hash functions at a streaming manner. Further, an iterative discrete optimization algorithm is derived to mine the inter-dependency between the label categories and discriminatively learn the hash codes without relaxation. Accordingly, the hash codes are adaptively learned online with the high discriminative capability and inter-dependency, while avoiding high computation complexity to process the streaming data. Experimental results show its outstanding performance in comparison with the-state-of-arts.
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