Learning of General Cases
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 variation in appearance of these objects. We present our conceptual clustering algorithm to learn a hierarchy of decreasingly generalized 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.
KeywordsCase Mining Case-Based Object Recognition Cluster Analysis
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