Abstract: In the big data era,application research on image retrieval technology for large-scale data is a hot field.Recent years,image hash algorithm has attracted much attention in large-scale image retrieval system in order to improve the retrieval efficiency and reduce the storage space.However,there are some issues with existing supervised hash code learning algorithms.Most of the supervised hash algorithms need to use image feature extractor for obtaining hand-crafted image features,which influence the effect of hash code training with image features,at the same time these methods cannot deal well with semantic similarity for image data set.With the development of deep learning research on the large-scale data,some recent related work try to deploy deep neural network to learn hash function and make the hash code training effect increased.But such kind of methods require carefully designed complex neural network structure thus increase the difficulty of the hash function design and cost more time on neural network training with large data set.These problems limit the range of the hash algorithm application with the deep learning architecture for large data sets.To solve the problems mentioned above,this paper proposed a fast image hashing algorithm based on deep convolution neural network.The proposed algorithm consists of an optimization approach for constructing the hash code of the training data set and a pre-trained large deep neural network for learning to improve the effect of hash algorithm,shor-tening the training time of complex neural network.According to the analysis of experimental results on different image data sets,both the effectiveness of hash function and the efficiency of training time of the proposed algorithm have better performance compared with the existing algorithms.

Key words: Deep learning,Image retrieval,Image hashing,Neural network,Optimization algorithm