Classification of image pixels based on minimum distance and hypothesis testing

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Show simple item record Ghimire, Santosh Wang, Haiyan 2012-07-19T15:05:50Z 2012-07-19T15:05:50Z 2012-07-19
dc.description.abstract In this article, we introduce a new method of image pixel classification. Our method is a nonparametric classification method which uses combined evidence from the multiple hypothesis testings and minimum distance to carry out the classification. Our work is motivated by the test-based classification introduced by Liao and Akritas [2007].We focus on binary and multiclass classification of image pixels taking into account of both equal and unequal prior probability of classes. Experiments show that our method works better in classifying image pixels in comparison with some of the standard classification methods such as linear discriminant analysis, quadratic discriminant analysis, classification tree, polyclass method, and Liao and Akritas’s method. We apply our classifier to perform image segmentation. Experiments show that our test-based segmentation has excellent edge detection and texture preservation property for both grey scale and color images. en_US
dc.relation.uri en_US
dc.subject Image processing en_US
dc.subject Image classification en_US
dc.subject Hypothesis testing en_US
dc.subject Minimum distance en_US
dc.subject Image segmentation en_US
dc.title Classification of image pixels based on minimum distance and hypothesis testing en_US
dc.type Article (author version) en_US 2012 en_US
dc.citation.doi en_US
dc.citation.epage 2287 en_US
dc.citation.issue 7 en_US
dc.citation.jtitle Computational Statistics and Data Analysis en_US
dc.citation.spage 2273 en_US
dc.citation.volume 56 en_US
dc.contributor.authoreid hwang en_US
dc.contributor.authoreid ghimire en_US

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