Learning Methodologies and Discriminating Visual Cues for Unsupervised Image Segmentation

In this paper, we describe our research in unsupervised image segmentation using machine learning techniques. First, we apply image processing techniques to extract from an image a set of training cases, which are histogram peaks described by their intensity ranges, and to compute spatial and textural attributes as visual cues. Second, we use learning by discovery methodologies to cluster these cases: COBWEB/3, SNOB, AutoClass, and APE. COBWEB/3 is based on incremental concept formation; AutoClass on Bayesian probabilities; and SNOB on minimum message length. APE is based on a new strategy called Aggregated Population Equalization that attempts to maintain similar strengths for all populations in its environment. Third, we obtain from the clustering results of the methodologies the number of visually significant classes in the image (and what these classes are) and finally segment the image. We conduct visual evaluation of the results to determine the best learning methodology and the set of discriminating visual cues for our remote sensing applications. Based on the findings, we have built an unsupervised image segmentation software tool called ASIS and have applied it to a range of remotely sensed images.