Deep hash image retrieval based on supervised semantic preservation

Abstract

In recent years, with the rapid increase of image data in the Internet, the requirements of image data storage and retrieval are increasingly high. Tra-ditional image retrieval is based on high-dimensional features of the image to carry out similarity (Euclidean distance or cosine distance) calculation for retrieval. Storage and retrieval cost is high, although there are some methods to achieve hash retrieval, but the accuracy is generally not very high. In this paper, a deep hashing coding method based on supervised learning is proposed. This method uses deep neural network to obtain approximate binary codes. After quantifying these approximate binary codes, it can simply and quickly search similar images from massive image data, thus realizing large-scale image retrieval technology. We are in general MNIST dataset, CIFAR-10 dataset, SUN397 dataset and large-scale visual dataset ILSVCR2012 commonly used evaluation test data sets, such as the experimental results showed that the proposed method can achieve good retrieval accuracy, the MAP value of CIFAR-10 compared with existing methods improved a lot, to prove the effectiveness of the proposed method.