A Fast Hash Image Retrieval Method Based on Dual Learning.

Aiming at the problems of low efficiency of feature extraction and insufficient use of feature correlation in image retrieval based on deep hashing, an image retrieval method Dual Learning Hashing(DLH) that combines feature layer learning and hash layer learning is proposed. In view of the problem that existing methods usually extract local features with intensive attention mechanism by focusing on dense local regions, these local regions cannot contain different local information, a module is developed to learn differentiated local features by locating the peaks of non-overlapping subdomains in the feature map. In order to make full use of semantic information and generate high quality hash code, quantization function and probabilistic semantic-preserving function are designed in the hash layer. Finally, combined with the two parts of learning, the total loss function is given. A large number of comparative experiments have been carried out on three widely used datasets. The experimental results show that compared with other advanced deep hash methods, DLH has achieved better retrieval performance.