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Publication
IEEE Transactions on Systems Science and Cybernetics
Paper
Feature Extraction on Binary Patterns
Abstract
The objects and methods of automatic feature extraction on binary patterns are briefly reviewed. An intuitive interpretation for geometric features is suggested whereby such a feature is conceived of as a cluster of component vectors in pattern space. A modified version of the Isodata or K-means clustering algorithm is applied to a set of patterns originally proposed by Block, Nilsson, and Duda, and to another artificial alphabet. Results are given in terms of a figure-of-merit which measures the deviation between the original patterns and the patterns reconstructed from the automatically derived feature set. Copyright © 1970 by The Institute of Electrical and Electronics Engineers, Inc.