Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data

Authors:
Despina Kontos;Vasileios Megalooikonomou
Affiliations:
Department of Computer and Information Sciences, Temple University, 319 Wachman Hall, 1805 North Broad Street, Philadelphia, PA 19122, USA;Department of Computer and Information Sciences, Temple University, 319 Wachman Hall, 1805 North Broad Street, Philadelphia, PA 19122, USA
Venue:
Pattern Recognition
Year:
2005

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Abstract

We propose a method for characterizing spatial region data. The method efficiently constructs a k-dimensional feature vector using concentric spheres in 3D (circles in 2D) radiating out of a region's center of mass. These signatures capture structural and internal volume properties. We evaluate our approach by performing experiments on classification and similarity searches, using artificial and real datasets. To generate artificial regions we introduce a region growth model. Similarity searches on artificial data demonstrate that our technique, although straightforward, compares favorably to mathematical morphology, while being two orders of magnitude faster. Experiments with real datasets show its effectiveness and general applicability.