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A Novel Multi-scale Invariant Descriptor Based on Contour and Texture for Shape Recognition

  • Jishan Guo
  • Yi Rong
  • Yongsheng Gao
  • Ying Liu
  • Shengwu XiongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11364)

Abstract

This paper proposes a novel multi-scale descriptor for shape recognition. The contour of shape is represented by a sequence of sample points with uniform spacing. Straight lines connected between two moving contour points are used to cut the shape. The lengths of the contour segments between the two sampled contour points determine the levels of scales. Then the geometric features of the cut contour and the interior texture features around the straight lines are extracted at each scale. This method not only has the powerful discriminability to describe a shape from coarse to fine, but also is invariant to scale, rotation, translation and mirror transformations. Experiments conducted on five image datasets (COIL-20, Flavia, Swedish, Leaf100 and ETH-80) demonstrate that the proposed method significantly outperforms the state-of-the-art methods.

Keywords

Multi-scale Shape recognition Contour Texture 

Notes

Acknowledgement

This work is partially supported by National Key R&D Program of China (No. 2016YFD0101903), National Natural Science Foundation of China (No. 61702386, 61672398), Major Technical Innovation Program of Hubei Province (No. 2017AAA122), Key Natural Science Foundation of Hubei Province of China (No. 2017CFA012), the Fundamental Research Funds for the Central Universities (No. 185210006).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jishan Guo
    • 1
  • Yi Rong
    • 1
  • Yongsheng Gao
    • 2
  • Ying Liu
    • 1
  • Shengwu Xiong
    • 1
    Email author
  1. 1.Wuhan University of TechnologyWuhanChina
  2. 2.School of EngineeringGriffith UniversityBrisbaneAustralia

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