Learning mixtures of point distribution models with the EM algorithm

✍ Scribed by Abdullah A. Al-Shaher; Edwin R. Hancock

• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 409 KB
• Volume: 36
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/s0031-3203(03)00139-0

✦ Synopsis

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 reÿnes the eigenvectors of the covariance matrix using a gradient ascent technique. Second, we show how recognition by alignment can be realised by ÿtting a mixture of linear shape deformations. We evaluate the method on the problem of learning the class-structure and recognising Arabic characters.