Learning mixtures of point distribution models with the EM algorithm |
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Authors: | Abdullah A. Edwin R. |
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Affiliation: | Department of Computer Science, University of York, York YO1 5DD, UK |
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Abstract: | 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. |
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Keywords: | Point distribution models Expectation maximization algorithm Unsupervised learning Alignment Shape recognition Arabic character |
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