A Boosting Method based on SVM for Relevance Feedback in Content-based 3D Model Retrieval

摘要：

The technique of relevance feedback has been introduced to content-based 3D model retrieval. Support Vector Machine as a learner is one of the classical approaches in relevance feedback. And the Boosting method, as one of the ensemble methods. can establish a strong leaner by combing the component learners. In this paper, a novel relevance feedback mechanism, which makes use of the main idea of boosting and the component SVM, is presented and applied to the content-based 3D model retrievaL The experiments, based on the 3D model database Princeton Shape Benchmark, show that the relevance feedback algorithm can improve the retrieval performance of traditional SvM in 3D model retrievaL

关键词： Content-based 3D model retrieval Relevance feedback Support Vector Machine Boosting

作者: Tao Wei Zheng Qin Xiaoman Cao Biao Leng

作者单位: Dept. of Computer Science and Tech.Tsinghua University, China School of Software Tsinghua University, China School of Computer Science and Engineering Beihang University, China

会议类型: 国际会议

会议名称: The 2nd International Conference on Software Engineering and Data Mining(IEEE 第二届国际软件工程和数据挖掘学术大会 SEDM 2010)

会议地点: 成都

会议语种:英文

页码: 468-473

在线出版日期: 2010-06-23（万方平台首次上网日期，不代表论文的发表时间）