Cartoon Image Creation System with Small Training Set and Its Application in Automatic Data Augmentation for Generative Adversarial Networks

Image generation based on artificial intelligence is one of the topics that have been widely discussed in the field of computer science in recent years. In this project, a framework that can automatically create cartoon images with small training datasets is proposed. The proposed system performs region segmentation and learns a region relationship tree from each learning image. The segmented regions are clustered automatically with an enhanced clustering mechanism with no prior knowledge of number of clusters. According to the topology represented by region relationship tree and clustering results, the regions are reassembled to create new images. A swarm intelligence optimization procedure is designed to coordinate the regions to the optimized sizes and positions in the created image. Rigid deformation using moving least squares is performed on the regions to generate more variety for created images. Compared with methods based on Generative Adversarial Networks, the proposed framework can create better images with limited computation resources and a very small amount of training samples. Furthermore, the proposed method can be applied to perform data augmentation for Generative Adversarial Networks to enhance the quality of generated images.