| dc.description.abstract | With the development of artificial intelligence in recent years, machine learning can be applied to more and more fields. Among them, deep learning is the most prominent, and has become the mainstream of machine learning in recent years.
This paper focuses on the automatic generation of cartoon images, and proposes a region clustering system for combined image generation. In the area of image generation, the image generation models used in most of the papers in recent years are based on deep learning, such as Generative Adversarial Network (GAN), Variational autoencoder (VAE), etc. This kind of image learning model based on deep learning has a very good generating capability, but usually requires a lot of training data and a long operation time, and the requirement of computing equipment is also expensive. For the general public, it usually depends on others to train a single-category generation model and is not possible to freely create multi-categories of images according to personal preferences.
The region clustering system proposed in this paper is intended to be applied to modular cartoon image creation We use the pre-trained convolutional neural network model to extract the features of input images’ regions, and then evaluating the cluster number of features by shallow network. At last, grouped these regions by unsupervised learning with the cluster number. Because of using shallow neural network, the computational cost and data volume requirements are lower compared to deep learning, and we don’t need any labels. By reducing the need for training data sets, the image generation system can more easily achieve multi-category image generation. The experimental results show that the system can automatically assess the number of better groupings and obtain good grouping results. | en_US |