Label consistent locally linear embedding based cross-modal hashing

Highlights：

• We maintain non-linear manifold structure of data when learning hash codes to effectively construct the approximate relationship between neighbors. It captures the feature based similarity consistency of heterogeneous cross-modal data and enhances the discriminative capability of hash codes.

• Instead of relaxing discrete constraints or learning the hash codes bit by bit, we generate the discrete binary codes directly by an iterative quantization method. Therefore, LCLCH can avoid large quantization error and make the optimization process more efficient.

摘要

•A novel discrete supervised hashing method is proposed for cross-modal retrieval. We utilize semantic label to guide the hashing learning process and make full use of the supervised information. In this way, it maintains label consistency to ensure the effectiveness of hash code learning.•We maintain non-linear manifold structure of data when learning hash codes to effectively construct the approximate relationship between neighbors. It captures the feature based similarity consistency of heterogeneous cross-modal data and enhances the discriminative capability of hash codes.•Instead of relaxing discrete constraints or learning the hash codes bit by bit, we generate the discrete binary codes directly by an iterative quantization method. Therefore, LCLCH can avoid large quantization error and make the optimization process more efficient.

论文评审过程：Received 31 May 2019, Revised 30 September 2019, Accepted 30 September 2019, Available online 7 October 2019, Version of Record 20 October 2020.