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IJMLC 2014 Vol.4(1): 68-72 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.388

Shape Classification Using Hybrid Regional and Global Descriptor

Cong Lin and Chi-Man Pun

Abstract—In this paper we focused on designing a novel hybrid regional descriptor for shape classification, which includes three novel descriptors regional area descriptor (RAD), regional skeleton descriptor (RSD) and tangent function(TF). The RAD and RSD are based on the primitive skeleton of shape while the tangent function is developed from the contour by finding out the important landmark points and collecting regional information around them. In the matching stage, a customized Optimal Path Searching algorithm is integrated into our setting as distance measure function, which is an extension of efficient dynamic programming algorithm. The proposed shape descriptors are tested on commonly used datasets and the results are analyzed and compared to state-of-the-art methods. The experimental results show that, compared to others, our results are satisfactory.

Index Terms—Shape classification, skeleton, contour, rad, rsd, tangent function, optimal path searching.

The authors are with Department of Computer and Information Science, University of Macau, China (e-mail: cmpun@umac.mo).


Cite:Cong Lin and Chi-Man Pun, "Shape Classification Using Hybrid Regional and Global Descriptor," International Journal of Machine Learning and Computing vol.4, no. 1, pp. 68-72, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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