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Classifying textile designs using region graphs

Classifying textile designs using region graphs

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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
DOIs
StatePublished - 2010

Abstract

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

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    2010. The copyright of this document resides with its authors.
    It may be distributed unchanged freely in print or electronic forms.

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