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 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.
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Jänichen, S., Perner, P. (2005). Learning of General Cases. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_125
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DOI: https://doi.org/10.1007/11590316_125
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
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