隨著近年來人工智慧的發展，機器學習所能應用的領域越來越廣泛，在這其中，尤以深度學習這塊最為突出，並且已成為近年來機器學習領域的主流，無論在圖像生成、生物識別、語意辨識…等，皆有相當優秀的表現，是個能夠廣泛應用於人工智慧各項領域的主流技術。
本篇論文關注於卡通圖像的自動生成，提出了一個應用於組合式圖像生成的部位區域分群系統。在圖像生成領域方面，近年來大多論文所使用的圖像生成模型都是基於深度學習，像是Generative Adversarial Network（GAN）、Variational autoencoder（VAE）、PixelCNN…等，其中GAN更是近兩年來的生成模型主流。這類基於深度學習的圖像生成模型其生成能力皆為相當優秀，但通常需要大量的訓練資料以及較長的運算時間，所需的運算設備也較為昂貴。對於一般大眾使用者來說，通常得仰賴於使用他人所訓練好的單一類別生成模型來進行創作，而無法隨意的依照個人喜好進行多種類別的圖像創作。
本篇論文所提出的部位區域（Region）分群系統是為了應用於組合式卡通圖像生成，先以預訓練的卷積神經網路模型提取輸入圖像部位特徵，再使用淺層網路評估特徵群數並以非監督式學習的方式來對其進行分群，故運算成本以及資料量需求與深度學習相比皆為較低，且不須任何樣本標記資訊。透過降低對訓練資料集的需求，使圖像生成系統能更加容易地達到多類別圖像生成。在實驗結果中顯示，本系統確實能自動評估出較好的分群群數並得到良好的分群結果。

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.

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