Using Learning by Discovery to Segment Remotely Sensed Images

In this paper, we describe our research in computer-aided image analysis. We have incorporated machine learning methodologies with traditional image processing to perform unsupervised image segmentation. 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 spatial and textural attributes. Second, we use learning by discovery methodologies to cluster these cases. The first methodology we use is based on COBWEB/3. The second methodology is based on an Aggregated Population Equalization (APE) strategy that attempts to maintain similar strengths for all populations in its environment. The clustering result of either approach tells us the number of visually significant classes in the image (and what these classes are) and thus enables us to perform unsupervised image segmentation. Based on the results of the visual evaluation of the segmented images, we have built an unsupervised segm...

• Type: Conference
• Year: 2000
• Where: ICML
• Authors: Leen-Kiat Soh, Costas Tsatsoulis
• Keywords: ICML 2000, Image, Image Processing, Machine Learning, Unsupervised Image Segmentation