Data driven composite shape descriptor design for shape retrieval with a VoR-Tree

  • Zi-hao Wang
  • Hong-wei Lin
  • Chen-kai Xu


We develop a data driven method (probability model) to construct a composite shape descriptor by combining a pair of scale-based shape descriptors. The selection of a pair of scale-based shape descriptors is modeled as the computation of the union of two events, i.e., retrieving similar shapes by using a single scale-based shape descriptor. The pair of scale-based shape descriptors with the highest probability forms the composite shape descriptor. Given a shape database, the composite shape descriptors for the shapes constitute a planar point set. A VoR-Tree of the planar point set is then used as an indexing structure for efficient query operation. Experiments and comparisons show the effectiveness and efficiency of the proposed composite shape descriptor.


shape descriptor shape retrieval shape analysis data-driven model 

MR Subject Classification

68P20 68U05 


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Copyright information

© Editorial Committee of Applied Mathematics-A Journal of Chinese Universities and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematical Science, State Key Lab. of CAD&CGZhejiang UniversityHangzhouChina

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