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

Mechanisms for visualizing image collections are essential for browsing and exploring their content. This is especially true when metadata are ineffective in retrieving items due to the sparsity or esoteric nature of text. An obvious approach is to automatically lay out sets of images in ways that reflect relationships between the items. However, dimensionality reduction methods that map from high-dimensional content-based feature distributions to low-dimensional layout spaces for visualization often result in displays in which many items are occluded whilst large regions are empty or only sparsely populated. Furthermore, such methods do not consider the shape of the region of layout space to be populated. This paper proposes a method, high-entropy layout distributions. that addresses these limitations. Layout distributions with low differential entropy are penalized. An optimization strategy is presented that finds layouts that have high differential entropy and that reflect inter-image similarities. Efficient optimization is obtained using a step-size constraint and an approximation to quadratic (Renyi) entropy. Two image archives of cultural and commercial importance are used to illustrate and evaluate the method. A comparison with related methods demonstrates its effectiveness.