Selective feature combination and automatic shape categorization of 3D models

Authors:
Tianyang Lv;Guobao Liu;Shao-bin Huang;Zheng-xuan Wang
Affiliations:
College of Computer Science and Technology, Harbin Engineering University, Harbin, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Harbin Engineering University, Harbin, China;College of Computer Science and Technology, Jilin University, Changchun, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Year:
2009

Citing 8
Cited 0

Modern Information Retrieval

Modern Information Retrieval
Ensembling neural networks: many could be better than all

Artificial Intelligence
A search engine for 3D models

ACM Transactions on Graphics (TOG)
X-means: Extending K-means with Efficient Estimation of the Number of Clusters

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Princeton Shape Benchmark (Figures 1 and 2)

SMI '04 Proceedings of the Shape Modeling International 2004
Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search

ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Feature Combination and Relevance Feedback for 3D Model Retrieval

MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Three-dimensional shape searching: state-of-the-art review and future trends

Computer-Aided Design

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is the key problems in 3D model retrieval to obtain good feature and classify models efficiently. Although many feature extraction methods have been proposed, none is adapted to all models. Moreover, it still relies on manual work to classify models. To solve these problems, firstly, the paper proposes a series of selective combination methods which automatically decide each feature's appropriate weight. The experiments conduct on PSB show that the combined feature performs much better than the best single feature. Secondly, the paper proposes the iterative clustering process to obtain the shape-based 3D models classification based on the combined feature. Experiment shows that the method can classify 91% models of Princeton Shape Benchmark.