The visual features of the state-of-the-art image retrieval methods lack of learning ability

which lead to low expression ability.And the efficiency of traditional index methods is fairly low for large image database.In view of this

an image retrieval method based on convolutional neural network and kernel-based supervised Hashing is proposed.Firstly

a large convolutional neural network is employed to learn the intrinsic implications of training images so as to improve the distinguish ability and expression ability of visual feature.Secondly

kernel-based supervised Hashing is applied to learn from the high-dimensional visual feature and map into low-dimensional hamming space and achieve compact Hash codes.Finally

image retrieval is accomplished in low-dimensional hamming space.Experimental results of ImageNet-1000 and Caltech-256 datasets indicate that the expression ability of visual feature is effectively improved and the image retrieval performance is substantially boosted compared with the state-of-the-art methods.