IDEAS home Printed from
   My bibliography  Save this article

Classification of image pixels based on minimum distance and hypothesis testing


  • Ghimire, Santosh
  • Wang, Haiyan


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 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, the polyclass method, and the Liao and Akritas 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 gray scale and color images.

Suggested Citation

  • Ghimire, Santosh & Wang, Haiyan, 2012. "Classification of image pixels based on minimum distance and hypothesis testing," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2273-2287.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:7:p:2273-2287 DOI: 10.1016/j.csda.2012.01.005

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Liao, Shu-Min & Akritas, Michael, 2007. "Test-based classification: A linkage between classification and statistical testing," Statistics & Probability Letters, Elsevier, vol. 77(12), pages 1269-1281, July.
    Full references (including those not matched with items on IDEAS)


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:56:y:2012:i:7:p:2273-2287. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.