Semi-Supervised Cross-Modal Hashing Based on Label Prediction and Distance Preserving

Unlabeled data can be easily collected and help to exploit the correlations among different modalities. Existing works tried to explore label information contained in unlabeled data, however most of them suffer from difficulties in separating samples from different categories and have great interference. This paper proposes a novel method named semi-supervised cross-modal hashing based on label prediction and distance preserving(SS-LPDP). First, we use the deep neural networks to extract the feature of the labeled data among different modalities and get the feature distribution of each category. Second, the similarity of the data among different modalities is maximized based on the extracted feature and the label information. A common objective function is proposed with distance preserving constraint, which can effectively separate data into different categories and reduce interference in retrieval. An optimization algorithm is used to update the network parameters of feature learning in each modality, and the label information of unlabeled data are dynamically updated according to the changes of the feature distribution in each iteration. Experimental evaluation on Wiki, Pascal and NUS-WIDE datasets show that the proposed method outperforms recent methods when we set 25% samples without category labels.