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Similarity pyramids for browsing and organization of large image databases

The advent of large image databases (> 10,000) has created a need for tools which can search and organize image automatically by their content. This paper presents a method for designing a hierarchical browsing environment which we call a similarity pyramid. The similarity pyramid groups similar images together while allowing users to view the database at varying levels of resolution. We show that the similarity pyramid is best constructed using agglomerative (bottom-up) clustering methods, and present a fast-sparse clustering method which dramatically reduces both memory and computation over conventional methods. We then present an objective measure of pyramid organization called dispersion, and we use it to show that our fast-sparse clustering method produces better similarity pyramids than top down approaches.