Similarity evaluation of graphic design based on deep visual saliency features

The creativity of an excellent design work generally comes from the inspiration and innovation of its main visual features. The similarity among primary visual elements stands as a paramount indicator when it comes to identifying plagiarism in design concepts. This factor carries immense importance, especially in safeguarding cultural heritage and upholding copyright protection. This paper aims 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 a visual saliency feature map from an input image and the other takes the output of the first to distinguish whether a visual saliency feature map is a predicted one or ground truth. Unlike 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 locate the optimal layout regions and notice insignificant regions. Priorities are then assigned to different visual elements. Experimental results show the proposed model obtains state-of-the-art performance among various similarity measurement methods.