Deep Hashing with Triplet Loss of Hash Centers and Dissimilar Pairs for Image Retrieval

Image hash coding learning based on deep neural network have a wide range of application scenarios and become a research hotspot in recent years. In the framework of most existing deep hashing methods, it is necessary to first design a loss function that can converge quickly, and then select an appropriate pre-training deep network and tune it. In order to balance global and local constraints, this paper combines the design of loss functions required by the processes of image feature learning and image hash coding, and adds the constraints of triple loss of hash centers and dissimilar pairs. The proposed novel loss function can give consideration to the characteristics of global hash centers, local dissimilarity of images and image classification labels in the training process. The pre-trained large models have been incorporated into the framework, which can extract image embeddings to assist in the generation of hash codes. The comparative experiments on several image datasets show that the DTLH method proposed in this paper can achieve better results than the traditional hashing methods and the deep hashing methods under different coding lengths.