Template-Based Shell Clustering Using a Line-Segment Representation of Data

Abstract

This paper presents the algorithms and experimental results for template-based shell clustering when the datasets are represented by line segments. Compared with point datasets, such representations have several advantages, which include better scalability and noise immunity, as well as the availability of orientation information. Using both synthetic and real-world image datasets, we have experimentally demonstrated that line-segment-based representations result in both better accuracy and better efficiency in shell clustering.