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Images have become an important data source in many scientific and commercial domains. Analysis and exploration of image collections often requires the retrieval of the best subregions matching a given query. The support of such content-based retrieval requires not only the formulation of an appropriate scoring function for defining relevant subregions but also the design of new access methods that can scale to large databases. In this paper, the authors propose a solution to this problem of querying significant image subregions. They design a scoring scheme to measure the similarity of subregions. Their similarity measure extends to any image descriptor. All the images are tiled and each alignment of the query and a database image produces a tile score matrix.
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