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Distance-Based Test Feature Classifiers and Its Applications
Vakhtang LASHKIA Shun'ichi KANEKO Stanislav ALESHIN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2000/04/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Processing, Image Pattern Recognition
learning, textual region location, Fourier feature, test feature classifier,
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In this paper, we present a class of combinatorial-logical classifiers called test feature classifiers. These are polynomial functions that can be used as pattern classifiers of binary-valued feature vectors. The method is based on so-called tests, sets of features, which are sufficient to distinguish patterns from different classes of training samples. Based on the concept of test we propose a new distance-based test feature classifiers. To test the performance of the classifiers, we apply them to a well-known phoneme database and to a textual region location problem where we propose a new effective textual region searching system that can locate textual regions in a complex background. Experimental results show that the proposed classifiers yield a high recognition rate than conventional ones, have a high ability of generalization, and suggest that they can be used in a variety of pattern recognition applications.