Classifying transformation-variant attributed point patterns

This paper presents a classification approach, where a sample is represented by a set of feature vectors called an attributed point pattern. Some attributes of a point are transformational-variant, such as spatial location, while others convey some descriptive feature, such as intensity. The proposed algorithm determines a distance between point patterns by minimizing a Hausdorff-based distance over a set of transformations using a particle swarm optimization. When multiple training samples are available for each class, we implement multidimensional scaling to represent the point patterns in a low-dimensional Euclidean space for visualization and analysis. Results are demonstrated for latent fingerprints from tenprint data and civilian vehicles from circular synthetic aperture radar imagery. (C) 2010 Elsevier Ltd. All rights reserved.