In this work, a framework that can automatically create cartoon images with low computation resources and 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.

在这项工作中，提出了一种框架，该框架可以自动创建具有低计算资源和小的训练数据集的卡通图像。所提出的系统执行区域分割并从每个学习图像中学习区域关系树。使用增强的聚类机制可以自动对分割的区域进行聚类，而无需事先了解聚类数量。根据区域关系树和聚类结果表示的拓扑，将区域重组以创建新图像。群体智能优化程序旨在将区域协调到所创建图像中的优化大小和位置。使用移动最小二乘法对区域进行刚性变形，以生成更多种类的图像。与基于生成对抗网络的方法相比，