International Conference on Pattern Recognition and Image Analysis

ICAPR 2005: Pattern Recognition and Image Analysis pp 473-483

3D Model Retrieval Based on Adaptive Views Clustering

  • Tarik Filali Ansary
  • Mohamed Daoudi
  • Jean-Phillipe Vandeborre
Conference paper

DOI: 10.1007/11552499_53

Volume 3687 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Ansary T.F., Daoudi M., Vandeborre JP. (2005) 3D Model Retrieval Based on Adaptive Views Clustering. In: Singh S., Singh M., Apte C., Perner P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg

Abstract

In this paper, we propose a method for 3D model indexing based on 2D views, named AVC (Adaptive Views Clustering). The goal of this method is to provide an optimal selection of 2D views from a 3D model, and a probabilistic Bayesian method for 3D model retrieval from these views. The characteristic views selection algorithm is based on an adaptive clustering algorithm and using statistical model distribution scores to select the optimal number of views. Starting from the fact that all views do not contain the same amount of information, we also introduce a novel Bayesian approach to improve the retrieval. We finally present our results and compare our method to some state of the art 3D retrieval descriptors on the Princeton 3D Shape Benchmark database.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tarik Filali Ansary
    • 1
  • Mohamed Daoudi
    • 2
  • Jean-Phillipe Vandeborre
    • 1
  1. 1.MIIRE Research Group(GET / INT / LIFL UMR USTL/CNRS 8022) 
  2. 2.Laboratoire d’Informatique de Tours (EA 2101)Université François-Rabelais