  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, we propose a
solution to this problem of querying significant image subregions. We design a
scoring scheme to measure the similarity of subregions. Our 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. We show that the
problem of finding the best connected subregion from this matrix is NP-hard and
develop a dynamic programming heuristic. With this heuristic, we develop two
index based scalable search strategies, TARS and SPARS, to query patterns in a
large image repository. These strategies are general enough to work with other
scoring schemes and heuristics. Experimental results on real image datasets
show that TARS saves more than 87% query time on small queries, and SPARS saves
up to 52% query time on large queries as compared to linear search. Qualitative
tests on synthetic and real datasets achieve precision of more than 80%.
