CONCEPTUAL CLUSTERING AND CASE GENERALIZATION OF TWO‐DIMENSIONAL FORMS

Abstract

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 of fungal spores. 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. We present results based on our application for health monitoring of biologically hazardous material.