A similarity-based approach for shape classification using Asian skeletons

Date

2010-10-01

Author

Erdem, Aykut
Tarı, Zehra Sibel

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Shape skeletons are commonly used in generic shape recognition as they capture part hierarchy, providing a structural representation of shapes. However, their potential for shape classification has not been investigated much. In this study, we present a similarity-based approach for classifying 2D shapes based on their Asian skeletons (Asian and Tan, 2005; Aslan et al., 2008). The coarse structure of this skeleton representation allows us to represent each shape category in the form of a reduced set of prototypical trees, offering an alternative solution to the problem of selecting the best representative examples. The ensemble of these category prototypes is then used to form a similarity-based representation space in which the similarities between a given shape and the prototypes are computed using a tree edit distance algorithm, and support vector machine (SVM) classifiers are used to predict the category membership of the shape based on computed similarities.

Subject Keywords

Signal Processing, Software, Artificial Intelligence, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/57970

Journal

PATTERN RECOGNITION LETTERS

DOI

https://doi.org/10.1016/j.patrec.2010.06.003

Collections

Department of Computer Engineering, Article

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Citation Formats

A. Erdem and Z. S. Tarı, “A similarity-based approach for shape classification using Asian skeletons,” PATTERN RECOGNITION LETTERS, pp. 2024–2032, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57970.