An artistic analysis model based on sequence cartoon images for scratch - Chai, Xiaolin; Sun, Yan; Luo, Hong; Guizani, Mohsen (International journal of intelligent systems, 2022)
With the development of visual programming languages, researchers pay attention to the automatic evaluation of visual projects. Previous work focus on the code evaluation but ignored another essential part—the visualization results. Scratch is a widely used programming platform, and projects created on it are displayed in the form of cartoon clips. It is valuable to explore the visual aesthetics embodied in these clips to fill the gap in the assessment system. We propose a model that predicts the human view scores of cartoon clips created on Scratch. Our method is divided into two steps to evaluate the aesthetic of the sequence images that compose cartoon clips. First, we train an image classification network to predict the relative aesthetics of individual images. Then we construct an aesthetic space for the sequence image and improve the rating within a specific range. We put forward ScratchGAN to generate a Scratch‐cartoon‐style aesthetic analysis data set for training the classification network. Experimental results show that our Generative Adversarial Network framework can well transform photos into a Scratch‐cartoon style. The single image assessment network can generate predictions that fit human cartoon aesthetic opinions. Our method achieves satisfactory results in the aesthetic evaluation of sequence cartoon images.