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

Authors:
Zhiwen Yu;Shaohong Zhang;Hau-San Wong;Jiqi Zhang
Affiliations:
Department of Computer Science, City University of, Hong Kong;Department of Computer Science, City University of, Hong Kong;Department of Computer Science, City University of, Hong Kong;Department of Computer Science, City University of, Hong Kong
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

Citing 9
Cited 1

Triangulating Simple Polygons and Equivalent Problems

ACM Transactions on Graphics (TOG)
Topology matching for fully automatic similarity estimation of 3D shapes

Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Shape distributions

ACM Transactions on Graphics (TOG)
A search engine for 3D models

ACM Transactions on Graphics (TOG)
Spin Images and Neural Networks for Efficient Content-Based Retrieval in 3D Object Databases

CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Using sets of feature vectors for similarity search on voxelized CAD objects

Proceedings of the 2003 ACM SIGMOD international conference on Management of data
3D model metrieval based on volumetric extended gaussian image and hierarchical self organizing map

MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Harmonic 3D shape matching

ACM SIGGRAPH 2002 conference abstracts and applications
3D head model classification by evolutionary optimization of the Extended Gaussian Image representation

Pattern Recognition

ROSy+: 3D Object Pose Normalization Based on PCA and Reflective Object Symmetry with Application in 3D Object Retrieval

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

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.