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A Filter-Refinement Scheme for 3D Model Retrieval Based on Sorted Extended Gaussian Image Histogram

  • Zhiwen Yu
  • Shaohong Zhang
  • Hau-San Wong
  • Jiqi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4571)

Abstract

In this paper, we propose a filter-refinement scheme based on a new approach called Sorted Extended Gaussian Image histogram approach (SEGI) to address the problems of traditional EGI. Specifically, SEGI first constructs a 2D histogram based on the EGI histogram and the shell histogram. Then, SEGI extracts two kinds of descriptors from each 3D model: (i) the descriptor from the sorted histogram bins is used to perform approximate 3D model retrieval in the filter step, and (ii) the descriptor which records the relations between the histogram bins is used to refine the approximate results and obtain the final query results. The experiments show that SEGI outperforms most of state-of-art approaches (e.g., EGI, shell histogram) on the public Princeton Shape Benchmark.

Keywords

Filter-refinement Extended Gaussian Image 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zhiwen Yu
    • 1
  • Shaohong Zhang
    • 1
  • Hau-San Wong
    • 1
  • Jiqi Zhang
    • 1
  1. 1.Department of Computer Science, City University ofHong Kong

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