Low-resource automatic cartoon image creation from limited samples

Hsu Yung Cheng, Chih Chang Yu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number102863
JournalJournal of Visual Communication and Image Representation
StatePublished - Aug 2020


  • Clustering
  • Convolutional neural networks
  • Image creation


Dive into the research topics of 'Low-resource automatic cartoon image creation from limited samples'. Together they form a unique fingerprint.

Cite this