首页> 外文会议>International Conference on Signal Processing(ICSP'06); 20061116-20; Guilin(CN) >3D Model Classification based on Multiple Features Integration
【24h】

3D Model Classification based on Multiple Features Integration

机译:基于多特征集成的3D模型分类

获取原文
获取原文并翻译 | 示例

摘要

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.
机译:在本文中,我们提出并评估了一种通过集成多个有效形状描述符的3D模型分类的新方法。在这种方法中,首先,将多个形状描述符分别传递给不同的模糊SVM分类器,然后从每个分类器中获得模糊隶属度。然后,将这些隶属度输入到BP神经网络,生成综合隶属度和最终分类决策。实验表明,该分类方法比传统的具有单一特征或单一分类器的3D模型分类方法具有更好的性能,证明了该方法在3D模型分类中的有效性和潜力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号