Finding Significant Subregions in Large Image Databases

TL;DR

This paper introduces scalable methods for identifying significant subregions in large image databases using a new scoring scheme and heuristics, enabling efficient content-based image retrieval.

Contribution

It proposes a novel scoring scheme for image subregions, proves NP-hardness of the problem, and develops scalable index-based search strategies TARS and SPARS.

Findings

TARS reduces query time by over 87% on small queries.

SPARS reduces query time by up to 52% on large queries.

Achieves over 80% precision in qualitative tests.

Abstract

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%.