A Fast Image Retrieval Method Based on Multi-Layer CNN Features

Based on the excellent performance of convolutional neural network in image feature representation, and deep hashing can meet the retrieval time requirement of large-scale image retrieval, this paper proposes an image retrieval algorithm combining convolutional neural network and deep hashing. The typical image retrieval algorithm only uses the full connection layer as the feature of image retrieval, and some of the samples have the retrieval accuracy of 0. We propose to fuse the information of different layers of a neural network as the feature representation of an image. Aiming at the problem of long response time when directly using image features for retrieval, we propose to use deep hashing to map the image features into binary hash codes, so that hash codes contain both the low-level edge information and high-level semantic information. Meanwhile, we propose a new similarity measure function for similarity matching. Compared with the existing image retrieval algorithms, experimental results show that our algorithm makes some improvements in retrieval accuracy. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.