Identification And Application, Based On Deformation Of The Image Orientation Of The Decision Tree

This thesis introduces a contract research for Omron Software Co. Ltd Japan. The research includes development of image characteristics describing image direction and then constructing an image direction classification system. In the area of contents based image classification, building the relationship between the low-leveled image characteristics and the high-leveled contents based concept is one of the most important works. In our research, variation of contour line is used for describing the characteristics of image structure. Using teacher images and machine learning method, an image direction classification model is built as a decision tree. Test results argued the validity of this method. Based on such model, we developed an image direction detecting system. The system also uses an object-oriented technique on highly abstract level, which implements easily extension of image characteristics and highly hidden of characteristic details, so as to ensure a relatively high system stability.Efforts in improving the capability of model are made. While the number of identified class increases, decision trees will become very large and deep. We deformed the decision tree to further improve the balance and the constructing efficiency of the decision tree. On the other hand, a method for auto-selecting of teacher images is implemented. The mechanism tries to select images near the boundary between different classes as well as the most uncertainty samples. The test results show the improvements in system capability.