Classification of image pixels based on minimum distance and hypothesis testing

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Show simple item record Ghimire, Santosh 2011-05-02T16:26:37Z 2011-05-02T16:26:37Z 2011-05-02
dc.description.abstract We introduce a new classification method that is applicable to classify image pixels. This work was motivated by the test-based classification (TBC) introduced by Liao and Akritas(2007). We found that direct application of TBC on image pixel classification can lead to high mis-classification rate. We propose a method that combines the minimum distance and evidence from hypothesis testing to classify image pixels. The method is implemented in R programming language. Our method eliminates the drawback of Liao and Akritas (2007).Extensive experiments show that our modified method works better in the classification of image pixels in comparison with some standard methods of classification; namely, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Classification Tree(CT), Polyclass classification, and TBC. We demonstrate that our method works well in the case of both grayscale and color images. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Hypothesis testing en_US
dc.subject minimum distance en_US
dc.subject image processing en_US
dc.subject image classification en_US
dc.title Classification of image pixels based on minimum distance and hypothesis testing en_US
dc.type Report en_US Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Haiyan Wang en_US
dc.subject.umi Statistics (0463) en_US 2011 en_US May en_US

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