基于三元组的跨模态深度哈希算法

ross-modal deep hash algorithm can cross the semantic gap between texts and images so that images and texts can be retrieved from each other. In order to improve the retrieval accuracy of existing cross-modal hash algorithms, this paper proposes a triple-based cross-modal hash model (DTCMH), which loss function takes into account the regular items that reduce the quantization error , the ranking loss based on triplets and the classification error loss in Softmax classifier. This model learns the ordering relationship and the semantic features of data. DTCMH performance is better than the state-of-the-art cross model algorithm.