DocumentCode :
461657
Title :
3D Model Classification based on Multiple Features Integration
Author :
Liu, Weibin ; Xing, Weiwei ; Yuan, Baozong ; Liu, Ming
Author_Institution :
Inst. of Inf. Sci., Beijing Jiaotong Univ.
Volume :
3
fYear :
2006
fDate :
16-20 2006
Abstract :
In this paper, we propose and evaluate a novel approach for 3D model classification by integrating multiple efficient shape descriptors. In this approach, first, multiple shape descriptors are passed to different fuzzy SVM classifiers separately, and the fuzzy membership degrees are obtained from each classifier; then, these membership degrees are input into a BP neural network, the integrated membership degree and the final classification decision are produced. Experiments show that the proposed classification approach has the better performance than the traditional 3D model classification methods with single feature or single classifier, which proves the validity and potential of the presented approach for 3D model ]´classification
Keywords :
backpropagation; fuzzy set theory; image classification; neural nets; 3D model classification methods; BP neural network; fuzzy SVM classifiers; multiple features integration; multiple shape descriptors; CADCAM; Computer aided manufacturing; Fuzzy neural networks; Graphics; Information science; Neural networks; Shape; Support vector machine classification; Support vector machines; Virtual manufacturing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, 2006 8th International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9736-3
Electronic_ISBN :
0-7803-9736-3
Type :
conf
DOI :
10.1109/ICOSP.2006.345791
Filename :
4129171
Link To Document :
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