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Machine Vision and Applications

, Volume 24, Issue 8, pp 1685–1704 | Cite as

CM-BOF: visual similarity-based 3D shape retrieval using Clock Matching and Bag-of-Features

  • Zhouhui LianEmail author
  • Afzal Godil
  • Xianfang Sun
  • Jianguo Xiao
Original Paper

Abstract

Content-based 3D object retrieval has become an active topic in many research communities. In this paper, we propose a novel visual similarity-based 3D shape retrieval method (CM-BOF) using Clock Matching and Bag-of-Features. Specifically, pose normalization is first applied to each object to generate its canonical pose, and then the normalized object is represented by a set of depth-buffer images captured on the vertices of a given geodesic sphere. Afterwards, each image is described as a word histogram obtained by the vector quantization of the image’s salient local features. Finally, an efficient multi-view shape matching scheme (i.e., Clock Matching) is employed to measure the dissimilarity between two models. When applying the CM-BOF method in non-rigid 3D shape retrieval, multidimensional scaling (MDS) should be utilized before pose normalization to calculate the canonical form for each object. This paper also investigates several critical issues for the CM-BOF method, including the influence of the number of views, codebook, training data, and distance function. Experimental results on five commonly used benchmarks demonstrate that: (1) In contrast to the traditional Bag-of-Features, the time-consuming clustering is not necessary for the codebook construction of the CM-BOF approach; (2) Our methods are superior or comparable to the state of the art in applications of both rigid and non-rigid 3D shape retrieval.

Keywords

3D shape retrieval Non-rigid  Bag-of-Features  Local feature 

Notes

Acknowledgments

This work has been supported by National Natural Science Foundation of China (Grant No. 61202230), China Postdoctoral Science Foundation (Grant No.: 2012M510274), the SIMA program and the Shape Metrology IMS.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhouhui Lian
    • 1
    Email author
  • Afzal Godil
    • 2
  • Xianfang Sun
    • 3
  • Jianguo Xiao
    • 1
  1. 1.Institute of Computer Science and TechnologyPeking UniversityBeijingPeople’s Republic of China
  2. 2.National Institute of Standards and TechnologyGaithersburgUSA
  3. 3.Cardiff UniversityWalesUK

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