Similarity Evaluation of Graphic Design Based on Deep Visual Saliency Features

Abstract

The creativity of an excellent design work generally comes from the inspiration and innovation of its main visual features. The similarity between the main visual elements is the most important indicator for detecting plagiarism of design concepts, which is important to protect cultural heritage and copyright. The purpose of this paper is to develop an efficient similarity evaluation scheme for graphic design. A novel deep visual saliency feature extraction generative adversarial network is proposed to deal with the problem of lack of training examples. It consists of two networks: one predicts visual a saliency feature map from an input image; the other takes the output of the first to distinguish whether a visual saliency feature map is a predicted one or ground truth. Different from traditional saliency generative adversarial networks, a residual refinement module is connected after the encoding and decoding network. Design importance maps generated by professional designers are used to guide the network training. A saliency-based segmentation method is developed to not only locate the optimal layout regions but also notice insignificant regions. Priorities are then assigned to different visual elements. Experimental results show that the proposed model obtains state-of-the-art performance among various similarity measurement methods.