关键词: 图像检索, 卷积神经网络, 哈希编码, 分级检索

Abstract: Aiming at the retrieval of images, a method based on Hash coding and Convolutional Neural Network（CNN） is proposed. The idea is to add a Hash layer to the CNN, and a coarse-to-fine search strategy is used to retrieval similar images. Firstly, the coarse-level search is operated according to the Hash coding to obtain the same or similar images to form a pool of m images. Then the Euclidean distances between the high-level semantic features of the m images and query image are calculated for fine-level search, thus achieving the ultimate retrieval purpose. The proposed method takes the loss of Hash layer as one of the optimization goals, and two features are combined for image retrieval, which makes up the shortcomings of time consuming and memory using of the existing methods. The results show that the proposed method outperforms the state-of-art algorithms on printed fabric and CIFAR-10 datasets.

Key words: image retrieval, Convolutional Neural Network（CNN）, Hash coding, hierarchical search