We propose a modified classifier that is based on the maximum a posteriori probability principle that is applied to images having the matrix normal distributions. These distributions have a special covariance structure, which is interpretable and easier to estimate than general covariance matrices. The modification is applicable when the estimated covariance matrices are still not well-conditioned. The proposed classifier is tested on synthetic images and on images of gas burner flames. The results of comparisons with other classifiers are also provided.
CITATION STYLE
Rafajłowicz, E. (2018). Classifiers for matrix normal images: Derivation and testing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10841 LNAI, pp. 668–679). Springer Verlag. https://doi.org/10.1007/978-3-319-91253-0_62
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