Intelligent Retrieval Method of Approximate Painting in Digital Art Field

Abstract

With the rapid development of Internet technology and the wide application of image acquisition equipment, the number of digital artwork images is exploding. The retrieval of near-similar artwork images has a wide application prospect for copyright infringement, trademark registration, and other scenes. However, compared with traditional images, these artwork images have the characteristics of high similarity and complexity, which lead to the retrieval accuracy not meeting the demand. To solve the above problems, an intelligent retrieval method of artwork image based on wavelet transform and dual propagation neural network (WTCPN) is proposed. Firstly, the original artwork image is replaced by the low-frequency subimage after wavelet transform, which not only removes redundant information and reduces the dimension of data but also suppresses random noise. Secondly, in order to make the network assign different competition winning units to different types of modes, the dual propagation neural network is improved by setting the maximum number of times of winning neurons. Experimental results show that the proposed method can improve the accuracy of image retrieval, and the recognition accuracy of verification set can reach over 91%.