IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2008i11p4966-4978.html
   My bibliography  Save this article

Localized empirical discriminant analysis

Author

Listed:
  • Cutillo, L.
  • Amato, U.

Abstract

Some empirical localized discriminant analysis methods for classifying images are introduced. They use spatial correlation of images in order to improve classification reducing the 'pseudo-nuisance' present in pixel-wise discriminant analysis. The result is obtained through an empirical (data driven) and local (pixel-wise) choice of the prior class probabilities. Local empirical discriminant analysis is formalized in a framework that focuses on the concept of visibility of a class that is introduced. Numerical experiments are performed on synthetic and real data. In particular, methods are applied to the problem of retrieving the cloud mask from remotely sensed images. In both cases classical and new local discriminant methods are compared to the ICM method.

Suggested Citation

  • Cutillo, L. & Amato, U., 2008. "Localized empirical discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4966-4978, July.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:11:p:4966-4978
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00212-0
    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

    as
    1. Kolaczyk, Eric D. & Ju, Junchang & Gopal, Sucharita, 2005. "Multiscale, Multigranular Statistical Image Segmentation," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1358-1369, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jacek Batog & Barbara Batog, 2021. "Typology and Development of Local Administrative Units: Spatial Discriminant Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 548-569.
    2. Nielsen, Jens D. & Rumí, Rafael & Salmerón, Antonio, 2009. "Supervised classification using probabilistic decision graphs," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1299-1311, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jonathan R. Bradley & Christopher K. Wikle & Scott H. Holan, 2017. "Regionalization of multiscale spatial processes by using a criterion for spatial aggregation error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 815-832, June.
    2. Holmström, Lasse & Pasanen, Leena & Furrer, Reinhard & Sain, Stephan R., 2011. "Scale space multiresolution analysis of random signals," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2840-2855, October.
    3. Hotz, Thomas & Marnitz, Philipp & Stichtenoth, Rahel & Davies, Laurie & Kabluchko, Zakhar & Munk, Axel, 2012. "Locally adaptive image denoising by a statistical multiresolution criterion," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 543-558.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    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:52:y:2008:i:11:p:4966-4978. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.