A Decision Tree Based Image Enhancement Instruction System for Producing Contemporary Style Images

In this paper, we have proposed an image enhancement method by contemporary aesthetics criteria, which enables computers to produce visually favorable images automatically. The contemporary aesthetics criteria is obtained through data mining algorithms such as decision tree, support vector machine, and neural networks. In order to make computers adjust the images automatically to make them match the contemporary aesthetics criteria, the tree-based classification method is proposed for enhancement instructions. Our proposed system finds the reasons in the tree why an input image is not perceptually favorable and give improvement instructions accordingly. The training features are based on enhancement instructions, such as color component, saturation, sharpness, and so on. Preprocessing methods are also proposed for a more efficient labeling and better accuracy for image classification. The training samples are from both contemporary style high aesthetics quality images and those are not, which more than 15,000 training samples are used. The accuracy of our proposed method is above 95 %. The experimental result shows our system can give users or computers appropriate enhancement instruction efficiently.補正完