Acquisition of Concept Descriptions by Conceptual Clustering

Case-based object recognition requires a general case of the object that should be detected. Real world applications such as the recognition of biological objects in images cannot be solved by one general case. A case-base is necessary to handle the great natural variations in the appearance of these objects. In this paper we will present how to learn a hierarchical case base of general cases. We present our conceptual clustering algorithm to learn groups of similar cases from a set of acquired structural cases. Due to its concept description it explicitly supplies for each cluster a generalized case and a measure for the degree of its generalization. The resulting hierarchical case base is used for applications in the field of case-based object recognition.