Earticle

초록

영어

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 [1, 2, 3, 4, 5]. In this paper we present an improvement of 3D shape retrieval methods based on bag-of features approach. These methods use this approach to integrate a set of features extracted from 2D views of the 3D objects using the SIFT (Scale Invariant Feature Transform [6]) algorithm into histograms 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 [6], for the BF-SIFT method proposed by Ohbuchi, et al., [2] and CM-BOF proposed by Zhouhui, et al., [3], show that the proposed approach performs better than the original approach.

목차

Abstract
 1. Introduction
 2. Presentation of BF-SIFT and CM-BOF Methods
  2.1 The BF-SIFT Method
  2.2 CM-BOF Method
 3. Improvements based on Local Codebooks
 4. Experiments and Results
 5. Conclusion and Perspectives
 Acknowledgements
 References

키워드

저자

• El Wardani Dadi [ University Mohammed First, Faculty of Sciences, LaRi Laboratory Oujda (Morocco) ]
• El Mostafa Daoudi [ University Mohammed First, Faculty of Sciences, LaRi Laboratory Oujda (Morocco) ]
• Claude Tadonki [ Mines ParisTech, Laboratoire de Recherche en Informatique Mathématiques et Systèmes, Fontainebleau (France) ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605