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

PATTERN RECOGNITION

卷:38

Article

Kontos, D ; Megalooikonomou, V

关键词: characterization;    regions of interest;    feature extraction;    classification;    similarity searches;    region growth model;    spatial databases;   

DOI  :  10.1016/j.patcog.2005.04.020

来源: Elsevier

【 摘 要 】

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. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

【 授权许可】

Free