IMAGE RETRIEVAL BASED ON DEEP CONVOLUTIONAL NEURAL NETWORKS AND BINARY HASHING LEARNING

With the increasing amount of image data, the image retrieval methods have several drawbacks, such as the low expression ability of visual feature, high dimension of feature, low precision of image retrieval and so on. To solve these problems, a learning method of binary hashing based on deep convolutional neural networks is proposed. The basic idea is to add a hash layer into the deep learning framework and simultaneously learn image features and hash functions which should satisfy independence and quantization error minimized. First, 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. Second, the visual feature is putted into the hash layer, in which hash functions are learned. And the learned hash functions should satisfy the classification error and quantization error minimized and the independence constraint. Finally, given an input image, hash codes are generated by the output of the hash layer of the proposed framework and large scale image retrieval can be accomplished in low-dimensional hamming space. Experimental results on the three benchmark datasets show that the binary hash codes generated by the proposed method has superior performance gains over other state-of-the-art hashing methods.