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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Jänichen, S., & Perner, P. (2005). Acquisition of concept descriptions by conceptual clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3587 LNAI, pp. 153–162). Springer Verlag. https://doi.org/10.1007/11510888_16
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