Asymmetric cross–modal hashing with high–level semantic similarity

Highlights：

• We propose a novel cross-modal hashing approach, which can effectively embed high level semantic similarity into the learning process of hash codes. A two-stage hash coding learning strategy is designed to optimize learning hash codes and hash functions respectively; our strategy effectively reduces the information loss in the process of instances mapping hash codes. To some extent, it improves the hash code learning rate of new samples, so our strategy can be extended to large-scale multi-mode information retrieval.

• We propose a novel semantic-enhanced scheme to make full leverage the label information and gain more powerful hash functions, our approach can generate more discriminative hash codes for new instances via powerful hash functions. we conduct comprehensive experiments on several datasets, experimental results demonstrate the superior effectiveness of our approach outperforms several state-of-the-art hashing models on either the retrieval accuracy or the hash learning efficiency.

摘要

•To give reliable supervisory signals for the hash learning procedure, we develop a simple but effective supervised cross-modal hash learning framework to learn more discriminative hash codes and preserve the semantic similarity. An effective iterative alternative optimization scheme is developed to solve the NP-hard optimization problem, the time consuming and space complexity of our approach is O(n).•We propose a novel cross-modal hashing approach, which can effectively embed high level semantic similarity into the learning process of hash codes. A two-stage hash coding learning strategy is designed to optimize learning hash codes and hash functions respectively; our strategy effectively reduces the information loss in the process of instances mapping hash codes. To some extent, it improves the hash code learning rate of new samples, so our strategy can be extended to large-scale multi-mode information retrieval.•We propose a novel semantic-enhanced scheme to make full leverage the label information and gain more powerful hash functions, our approach can generate more discriminative hash codes for new instances via powerful hash functions. we conduct comprehensive experiments on several datasets, experimental results demonstrate the superior effectiveness of our approach outperforms several state-of-the-art hashing models on either the retrieval accuracy or the hash learning efficiency.