Abstract:

Most existing deep supervised hashing algorithms in image retrieval fail to effectively utilize difficult samples and the supervised information.In order to solve the this problem,an end-to-end asymmetric compression hashing algorithm is proposed which divides the output space of the network into the query set and database set,constructs the supervised data matrix,and effectively uses the global supervised information in an asymmetric way.Meanwhile,the gathering degree of within-class hash codes and the separation degree of inter-class hash codes are explicitly constrained in the loss function,which improves the discriminative ability of the model on difficult samples under training.First,the hashing layer and thresholding layer are added into the improved backbone feature extraction network,SKNet-50,which outputs the query set matrix.Then,the matrix of the database set is obtained by optimizing the loss function with the alternating direction method of multipliers(ADMM).Finally,the deep model is trained with the alternative optimization method.The proposed method can achieve 0.946,0.923 and 0.811 MAP on the CIFAR-10,NUS-WIDE and MS-COCO datasets,respectively,when the 48-bit hash code is used to retrieve images.Experimental results show that the proposed method can learn more discriminative and compact hash codes,and that the retrieval accuracy is superior to the current mainstream algorithm.

Key words: asymmetric hashing, image retrieval, deep learning