Shape Matching Using Point Context and Contour Segments

  • Christian Feinen
  • Cong Yang
  • Oliver Tiebe
  • Marcin GrzegorzekEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9006)


This paper proposes a novel method to generate robust contour partition points and applies them to produce point context and contour segment features for shape matching. The main idea is to match object shapes by matching contour partition points and contour segments. In contrast to typical shape context method, we do not consider the topological graph structure since our approach is only considering a small number of partition points rather than full contour points. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and contour deformations. The most significant scientific contributions of this paper include (i) the introduction of a novel partition point extraction technique for point context and contour segments as well as (ii) a new fused similarity measure for object matching and recognition, and (iii) the impressive robustness of the method in an object retrieval scenario as well as in a real application for environmental microorganism recognition.


Shape Descriptor Shape Match Contour Point Shape Retrieval Shape Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Research activities leading to this work have been supported by the China Scholarship Council, the German Research Foundation (DFG) within the Research Training Group 1564. We greatly thank M.Sc Chen Li from the University of Siegen for providing us with the Environmental Microorganism image dataset for experiments. We are also very grateful to Dr. Kimiaki Shirahama from University of Siegen for his guiding on significant technologies.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christian Feinen
    • 1
  • Cong Yang
    • 1
  • Oliver Tiebe
    • 1
  • Marcin Grzegorzek
    • 1
    Email author
  1. 1.Research Group for Pattern Recognition, Department ETIUniversity of SiegenSiegenGermany

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