Multipurpose image recognition based on active search and adaptive fuzzy inference neural network

This paper presents a versatile image recognition method, combining conventional pattern matching and a learning system. The proposed system first quickly determines candidate regions containing the target object in the image, and then examines the result by using the learning system. In the pattern matching, "active search" is used, which quickly extracts the target object using multiple two-dimensional reference images. As a learning system, an adaptive fuzzy inference neural network (AFINN) is used, which can perform knowledge processing. The AFINN automatically constructs the inference model, such as the selection of the necessary input information, and automatically acquires the inference rules through learning. Consequently, by applying the AFINN to the image recognition problem, highly versatile object recognition is realized without requiring explicit knowledge for recognition. The proposed system can be applied to a wide range of problems, since diversified objects can be recognized by simply modifying the registered reference images. In this paper, the method is applied to a sample problem, consisting of an image of the laboratory containing various objects, such as computers, monitors, and chairs, and it is verified that high recognition accuracy can be realized.