We propose an approach to categorize real-world natural scenes based on a semantic typicality measure. The proposed typicality measure allows to grade the similarity of an image with respect to a scene category. We argue that such a graded decision is appropriate and justified both from a human's perspective as well as from the image-content point of view. The method combines bottom-up information of local semantic concepts with the typical semantic content of an image category. Using this learned category representation the proposed typicality measure also quantifies the semantic transitions between image categories such as coasts, river s/lakes, forest, plains, mountains or sky/clouds. The method is evaluated quantitatively and qualitatively on a database of natural scenes. The experiments show that the typicality measure well represents the diversity of the given image categories as well as the ambiguity in human judgment of image categorization.
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