3D shape Retrieval Using Bag-of-Feature Method Basing on Local Codebooks

  • El Wardani Dadi
  • El Mostafa Daoudi
  • Claude Tadonki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)


Recent investigations illustrate that view-based methods, with pose normalization pre-processing get better performances in retrieving rigid models than other approaches and still the most popular and practical methods in the field of 3D shape retrieval [9,10,11,12]. In this paper we present an improvement of the BF-SIFT method proposed by Ohbuchi et al [1]. This method is based on bag-of-features to integrate a set of features extracted from 2D views of the 3D objects using the SIFT (Scale Invariant Feature Transform [2]) algorithm into a histogram using vector quantization which is based on a global visual codebook. In order to improve the retrieval performances, we propose to associate to each 3D object its local visual codebook instead of a unique global codebook. The experimental results obtained on the Princeton Shape Benchmark database [3] show that the proposed method performs better than the original method.


3D Content-based Shape Retrieval bag-of-features SIFT vector quantization codebook 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • El Wardani Dadi
    • 1
  • El Mostafa Daoudi
    • 1
  • Claude Tadonki
    • 2
  1. 1.Faculty of Sciences, LaRi LaboratoryUniversity of Mohammed FirstOujdaMorocco
  2. 2.Laboratory of Research in Computer, Math & SystemMines ParisTechFontainebleauFrance

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