Computer Science – Databases
Scientific paper
2009-06-19
Extending Database Technology (EDBT) 2010
Computer Science
Databases
16 pages, 48 figures
Scientific paper
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%.
Bhattacharya Arnab
Singh Ambuj K.
Singh Vishwakarma
No associations
LandOfFree
Finding Significant Subregions in Large Image Databases does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Finding Significant Subregions in Large Image Databases, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Finding Significant Subregions in Large Image Databases will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-401053