Journal
PATTERN RECOGNITION
Volume 36, Issue 12, Pages 2805-2818Publisher
ELSEVIER SCI LTD
DOI: 10.1016/S0031-3203(03)00139-0
Keywords
point distribution models; expectation maximization algorithm; unsupervised learning; alignment; shape recognition; Arabic character
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This paper demonstrates how the EM algorithm can be used for learning and matching mixtures of point distribution models. We make two contributions. First, we show how shape-classes can be learned in an unsupervised manner. We present a fast procedure for training point distribution models using the EM algorithm. Rather than estimating the class means and covariance matrices needed to construct the PDM, the method iteratively refines the eigenvectors of the covariance matrix using a gradient ascent technique. Second, we show how recognition by alignment can be realised by fitting a mixture of linear shape deformations. We evaluate the method on the problem of learning the class-structure and recognising Arabic characters. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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