Asymmetric Gaussian and Its Application to Pattern Recognition

Authors:
Tsuyoshi Kato;Shin'ichiro Omachi;Hirotomo Aso
Affiliations:
-;-;-
Venue:
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2002

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Abstract

In this paper, we propose a new probability model, 'asymmetric Gaussian(AG),' which can capture spatially asymmetric distributions. It is also extended to mixture of AGs. The values of its parameters can be determined by Expectation-Conditional Maximization algorithm. We apply the AGs to a pattern classification problem and show that the AGs outperform Gaussian models.