Abstract: In recent years,hashing algorithm have been widely concerned in efficient cross-modal retrieval of large-scale multimedia data due to small storage costs and high retrieval speed.Most of the existing cross-modal hashing algorithms are supervised or unsupervised methods,and supervised methods usually achieve better performance.However,in real world applications,it is not feasible to require all data to be labeled.In addition,most of these methods are offline,which need to pay high training costs and are very inefficient when facing input of large stream data.This paper proposes a new semi-supervised cross-modal hashing me-thod－－online semi-supervised anchor graph cross-modal hashing(OSAGCH),which builds a semi-supervised anchor graph cross-modal hashing model.It uses regularized anchor graphs to predict data labels in the case where only part of the data has labels,and uses subspace relationship learning to learn hash functions,generating a unified hash code by one step.Then the model is expanded to online version for streaming data input,allowing it to process streaming data.Experiments on public multi-modal data sets indicate that the performance of proposed method is superior to other existing methods.

Key words: Cross-modal hashing, Semi-supervised learning, Anchor graph regularization, Online learning, Subspace learning