Shape Retrieval Using Statistical Chord-Length Features

  • Chaojian Shi
  • Bin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


A novel shape description method, statistical chord-length features (SCLF), is proposed for shape retrieval. SCLF first describes the contour of a 2D shape using k/2 one-dimensional chord-length functions derived from partitioning the contour into k arcs of the same length, where k is the parameter of SCLF. The means and variances of all the chord-length functions are then calculated and a k dimensional feature vector is generated as a shape descriptor. Two experiments are conducted and the results show that SCLF achieves higher retrieval performance than traditional description methods such as geometric moment invariants and Fourier descriptors.


Image Retrieval Chord Length Shape Descriptor Fourier Descriptor Shape Retrieval 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chaojian Shi
    • 1
    • 2
  • Bin Wang
    • 2
  1. 1.Merchant Marine CollegeShanghai Maritime UniversityShanghaiP.R. China
  2. 2.Department of Computer Science and EngineeringFudan UniversityShanghaiP.R. China

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