To date most research in image processing has been based on quantitative representations of image features using pixel values, however, humans often use abstract and semantic knowledge to describe and analyze images. To enhance cognitive adequacy and tractability, we here present a multilayer framework based on qualitative spatial models. The layout features of segmented images are defined by qualitative spatial models which we introduce, and represented as a set of qualitative spatial constraints. Assigned different semantic and context knowledge, the image segments and the qualitative spatial constraints are interpreted from different perspectives. Finally, the knowledge layer of the framework enables us to describe the image in a natural way by integrating the domain-specified semantic constraints and the spatial constraints.
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