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Beta Mixture Models And The Application To Image Classification
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2009 (English)In: 2009 16th IEEE International Conference On Image Processing, vols 1-6, 2009, 2021-2024 p.Conference paper (Refereed)
Abstract [en]

Statistical pattern recognition is one of the most studied and applied approaches in the area of pattern recognition. Mixture modelling of densities is an efficient statistical pattern recognition method for continuous data. We propose a classifier based on the beta mixture models for strictly bounded and asymmetrically distributed data. Due to the property of the mixture modelling, the statistical dependence in a multi-dimensional variable is captured, even with the conditional independence assumption in each mixture component. A synthetic example and the USPS handwriting digit data was used to verify the effectiveness of this approach. Compared to the conventional Gaussian mixture models (GMM), the beta mixture models has a better performance on data which has strictly bounded value and asymmetric distribution. The performance of beta mixture models is about equivalent to that of GMM applied to data transformed via a strictly increasing link function.

Place, publisher, year, edition, pages
2009. 2021-2024 p.
Keyword [en]
Mixture Models, Beta Distribution, EM Algorithm, Gray Image, USPS Data
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-29634DOI: 10.1109/ICIP.2009.5414043ISI: 000280464300506ScopusID: 2-s2.0-77951949412ISBN: 978-1-4244-5653-6OAI: diva2:408325
16th IEEE International Conference on Image Processing Location: Cairo, Egypt, Date: NOV 07-10, 2009
Available from: 2011-04-04 Created: 2011-02-11 Last updated: 2011-09-29Bibliographically approved

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