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
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 language | English |
|---|---|
| Title of host publication | British Machine Vision Conference |
| Publisher | British Machine Vision Association and Society for Pattern Recognition |
| Number of pages | 10 |
| ISBN (Electronic) | 1-901725-40-5 |
| DOIs | |
| Publication status | Published - 2010 |