Unsupervised classification using spatial region growing segmentation and fuzzy training

This study has presented an approach to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. Region growing segmentation and local fuzzy classification have been employed to find the sample classes that well represent the true image. The segmentation algorithm makes use of spatial contextual information in a hierarchical clustering procedure and multi-window operation using a pyramid-like structure to increase the computational efficiency. The fuzzy classification, which conducts classification by iteratively identifying expected maximum likelihood parameters of the class, is applied for the segmented regions in order to determine the sample classes. The maximum likelihood classifier has been used the unlabelled regions to assign them into one of a finite number of classes. The algorithm has been evaluated with simulated image data with various class patterns.