The Potential of Contour Grouping for Image Classification

An image classification system is introduced, that is predominantly based on a description of contours and their relations. A contour is described by geometric parameters characterizing its global aspects (arc or alternating) and its local aspects (degree of curvature, edginess, symmetry). To express the relation between contours, we use a multi-dimensional vector, whose parameters describe distances between contour points and the contours' local aspects. This allows comparing for instance L features or parallel contours with a simple distance measure. The approach has been evaluated on two image collections (Caltech 101 and Corel) and shows a reasonable categorization performance, yet its future lies in exploiting the preprocessing to understand 'parts' of the image.

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