Abstract
This paper describes a shape representation technique for learning shape classes. This representation technique is based on the notion of representing categorical shape knowledge; shape itself is represented by so-called conjunctions of local properties (CLP). Shape concepts are learned by a technique called property-based learning, an incremental learning method that inductively selects properties crucial for classification. Unlike other classification methods based on distances or similarities, classification performance does not degrade as the number of classes increases and classification can be done correctly with only partial information of instances.
Using this shape representation, shape prototypes can be learned and shapes can be classified in the presence of viewpoint changes, local movements (such as moving handles of pliers or fingers) and occlusion.
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© 1994 Springer-Verlag Berlin Heidelberg
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Dunn, S.M., Cho, K. (1994). Learning Shape Classes. In: O, YL., Toet, A., Foster, D., Heijmans, H.J.A.M., Meer, P. (eds) Shape in Picture. NATO ASI Series, vol 126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03039-4_35
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DOI: https://doi.org/10.1007/978-3-662-03039-4_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-08188-0
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