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Issue No. 09 - September (1994 vol. 16)
ISSN: 0162-8828
pp: 882-888
<p>This paper is a summary of a new approach to learning shape concepts. In this system, a shape is represented by conjunctions of local shape properties. Conjunctions of local properties are consistent and unique for distinct shapes and are robust enough to represent shape in the presence of occlusion. A new learning method, called property based learning, is developed and used to learn conjunctions of local properties. Unlike other classification methods based on distances or similarities, classification performance does not degrade linearly as the number of classes increases and classification can be done correctly with only partial information of instances. Property based learning is an incremental learning method that selects properties crucial for classification. Two experiments are reported. In the first experiment with tool shapes, this shape learning system is used to classify shapes in the presence of view point changes, local movements such as moving handles of pliers, and occlusion. In the second experiment with hand gestures, the system can classify different gestures regardless of the movement in joints, fingers, and palms.</p>
pattern recognition; learning (artificial intelligence); learning systems; shape classes learning; local shape properties; occlusion; property based learning; classification; incremental learning; shape learning system; hand gestures

K. Cho and S. Dunn, "Learning Shape Classes," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 882-888, 1994.
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