A Contextual Classifier That Only Requires One Prototype Pixel for Each Class

Gabriela Mariel Maletti, Bjarne Kjær Ersbøll, Knut Conradsen

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    Abstract

    A three-stage scheme for the classification of multispectral images is proposed. In each stage, the statistics of each class present in the image are estimated. The user is required to provide only one prototype pixel for each class to be seeded into a homogeneous region. The algorithm starts by generating optimum initial training sets, one for each class, maximizing the redundancy in the data sets. These sets are the realizations of the maximal discs centered on the prototype pixels for which it is true that all the elements belong to the same class as the center one. Afterwards, a region-growing algorithm increases the sample size, providing more statistically valid samples of the classes. Final classification of each pixel is done by comparison of the statistical behavior of the neighborhood of each pixel with the statistical behavior of the classes. A critical sample size obtained from a model constructed with experimental data is used in this stage. The algorithm was tested with the Kappa coefficient κ on synthetic images and compared with K-means (κ~=0.41) and a similar scheme that uses spectral means (κ~=0.75) instead of histograms (κ~=0.90). The results are shown on a dermatological image with a malignant melanoma.
    Original languageEnglish
    JournalI E E E Transactions on Nuclear Science
    Volume49
    Issue number3
    Pages (from-to)700-706
    ISSN0018-9499
    DOIs
    Publication statusPublished - 2002

    Bibliographical note

    Copyright: 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

    Keywords

    • supervised classification
    • Prototypes
    • window size optimization
    • region growing
    • redundancy

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