Classifying textile designs using region graphs

Wei Jia, Stephen McKenna, Annette Ward, Keith Edwards

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

    530 Downloads (Pure)


    Markov random field pixel labelling is often used to obtain image segmentations in which each segment or region is labelled according to its attributes such as colour or texture. This paper explores the use of such a representation for image classification. In particular, the problem of classifying textile images according to design type is addressed. Regions with the same label are treated as a group and each group is associated uniquely with a vertex in an undirected, weighted graph. Each region group is represented
    as a bag of shape descriptors. Edges in the graph denote either the extent to which the groups’ regions are spatially adjacent or the dissimilarity of their respective bags of shapes. Series of unweighted graphs are obtained by removing edges in order of weight. Finally, an image is represented using its shape descriptors along with features derived from the chromatic numbers or domination numbers of the unweighted graphs and their complements. Experimental results are reported on a challenging classification task using
    images from a textile design archive
    Original languageEnglish
    Title of host publicationBritish Machine Vision Conference
    PublisherBritish Machine Vision Association and Society for Pattern Recognition
    Number of pages10
    ISBN (Electronic)1-901725-40-5
    Publication statusPublished - 2010


    Dive into the research topics of 'Classifying textile designs using region graphs'. Together they form a unique fingerprint.

    Cite this