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Images have become an important data source in many scientific and commercialdomains. Analysis and exploration of image collections often requires theretrieval of the best subregions matching a given query. The support of suchcontent-based retrieval requires not only the formulation of an appropriatescoring function for defining relevant subregions but also the design of newaccess methods that can scale to large databases. In this paper, we propose asolution to this problem of querying significant image subregions. We design ascoring scheme to measure the similarity of subregions. Our similarity measureextends to any image descriptor. All the images are tiled and each alignment ofthe query and a database image produces a tile score matrix. We show that theproblem of finding the best connected subregion from this matrix is NP-hard anddevelop a dynamic programming heuristic. With this heuristic, we develop twoindex based scalable search strategies, TARS and SPARS, to query patterns in alarge image repository. These strategies are general enough to work with otherscoring schemes and heuristics. Experimental results on real image datasetsshow that TARS saves more than 87% query time on small queries, and SPARS savesup to 52% query time on large queries as compared to linear search. Qualitativetests on synthetic and real datasets achieve precision of more than 80%.

Finding Significant Subregions in Large Image Databases