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This work introduces a conceptual representation for complex spatial arrangements of image features in large multimedia datasets. A novel data structure, termed the spatial event cube (SEC), is formed from the co-occurrence matrices of perceptually classified features with respect to specific spatial relationships. A visual thesaurus constructed using supervised and unsupervised learning techniques is used to label the image features. SECs can be used to not only visualize the dominant spatial arrangements of feature classes but also discover non-obvious configurations. SECs also provide the framework for high-level data mining techniques such as using the generalized association rule approach. Experimental results are provided for a large dataset of aerial images.