Using Filtering Algorithm for Partial Similarity Search on 3D Shape Retrieval System

  • Yingliang Lu
  • Kunihiko Kaneko
  • Akifumi Makinouchi
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 6)

Because 3D models are increasingly created and designed using computer graphics, computer vision, CAD medical imaging, and a variety of other applications, a large number of 3D models are being shared and offered on the Web. Large databases of 3D models, such as the Princeton Shape Benchmark Database [1], the 3D Cafe repository [2], and Aim@Shape network [3], are now publicly available. These datasets are made up of contributions from the CAD community, computer graphics artists, and the scientific visualization community. The problem of searching for a specific shape in a large database of 3D models is an important area of research. Text descriptors associated with 3D shapes can be used to drive the search process [4], as is the case for 2D images [5]. However, text descriptions may not be available, and furthermore may not apply for part-matching or similarity-based matching. Several content-based 3D shape retrieval algorithms have been proposed [6–8].

In this chapter, we propose a novel filtering method to filter out shapes. The proposed method is based on geometrical information rather than on topological information alone. Shapes are removed from the candidate pool if the processing part of the key shape is not similar to any part of the potential candidate shape. We select as partly similar shapes, those shapes that have the greatest number of similar parts. In addition, the method is herein implemented on a curve-skeleton thickness histogram (CSTH) [10] based 3D shape search. Therefore, the method can also be easily implemented on other multibranch complex graph matching applications if there are heavy values on the curves.

The remainder of the chapter is organized as follows. Section 34.2 provides an overview of related work in skeleton generation and content-based retrieval. In Sect. 34.3 we describe the CSTH. In addition, we describe the segment thickness histogram (STH) and the postprocession of CSTH to produce STH. In Sect. 34.4, we describe the novel filtering algorithm and the partial similarity shape retrieval method based on the shape’s STHs mentioned in Sect. 34.3. A discussion of an empirical study and the results thereof are presented in Sect. 34.5. Finally, in Sect. 34.6, we conclude the chapter and present ideas for future study.


Topological Information Filter Algorithm Partial Similarity Shape Retrieval Candidate Pool 
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 Science+Business Media, LLC 2008

Authors and Affiliations

  • Yingliang Lu
    • 1
  • Kunihiko Kaneko
    • 1
  • Akifumi Makinouchi
    • 2
  1. 1.Graduate School of Information Science and Electrical EngineeringKyushu UniversityFukuokaJapan
  2. 2.Department of Information and Network EngineeringKurume Institute of TechnologyFukuokaJapan

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