Regular texture analysis as statistical model selection

Junwei Han, Stephen J. McKenna, Ruixuan Wang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    13 Citations (Scopus)


    An approach to the analysis of images of regular texture is proposed in which lattice hypotheses are used to define statistical models. These models are then compared in terms of their ability to explain the image. A method based on this approach is described in which lattice hypotheses are generated using analysis of peaks in the image autocorrelation function, statistical models are based on Gaussian or Gaussian mixture clusters, and model comparison is performed using the marginal likelihood as approximated by the Bayes Information Criterion (BIC). Experiments on public domain regular texture images and a commercial textile image archive demonstrate substantially improved accuracy compared to two competing methods. The method is also used for classification of texture images as regular or irregular. An application to thumbnail image extraction is discussed.

    Original languageEnglish
    Title of host publicationComputer Vision – ECCV 2008
    Subtitle of host publication10th European Conference on Computer Vision Marseille, France, October 12-18, 2008. Proceedings, Part IV
    EditorsDavid Forsyth, Philip Torr, Andrew Zisserman
    Place of PublicationBerlin
    Number of pages14
    ISBN (Electronic)9783540886938
    ISBN (Print)9783540886921
    Publication statusPublished - 2008
    Event10th European Conference on Computer Vision - Palais des Congrès Parc Chanot, Marseille, France
    Duration: 12 Oct 200818 Oct 2008

    Publication series

    NameLecture notes in computer science
    ISSN (Print)0302-9743


    Conference10th European Conference on Computer Vision
    Abbreviated titleECCV 2008
    Internet address


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