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Hashing have had been successfully applied to different methods regarding a wide range of problems. It is fairly very effective for large-scale image retrieval in reducing the processing time. Although a number of hashing methods have been developed in recent years. Most of them the methods are based on hand-crafted features, which might not be optimally compatible with the hashing procedure for dealing with large datasets. Furthermore, recently, deep hash learning has been proposed to generate hash code, simultaneously, extracting to better extract the image features, which has shown better performance than the traditional methods of hand-crafted features. In this paper, we propose a new supervised hashing framework based on deep Residual Networks and kernel-based supervised hashing (KSH). Firstly, we exploit the learning abilities of deep residual network to mine the inherent hidden relationship of image content, extract deep feature descriptors, and increase the visual expression of images Secondly, kernel-based supervised hashing is applied to learn from the high-dimensional image 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 MNIST, CIFAR-10, CIFAR-100 and Caltech 256 show that the expression ability of visual feature is effectively improved and the image retrieval performance is substantially boosted compared with other related methods.

Image Retrieval Based On ResNet and KSH