Kontos Despina, Megalooikonomou Vasileios
Department of Computer and Information Sciences, Temple University, 319 Wachman Hall, 1805 N. Broad St., Philadelphia, PA 19122, USA, Email:
Pattern Recognit. 2005 Nov;38(11):1831-1846. doi: 10.1016/j.patcog.2005.04.020.
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.
我们提出了一种用于表征空间区域数据的方法。该方法利用从区域质心辐射出的3D同心球体（2D中的圆）高效地构建k维特征向量。这些特征捕获了结构和内部体积属性。我们通过使用人工和真实数据集进行分类和相似性搜索实验来评估我们的方法。为了生成人工区域，我们引入了一种区域增长模型。对人工数据的相似性搜索表明，我们的技术虽然简单，但与数学形态学相比具有优势，而且速度快两个数量级。对真实数据集的实验表明了其有效性和普遍适用性。
