Journal of Systems Engineering and Electronics ›› 2009, Vol. 31 ›› Issue (7): 1733-1737.

• 软件、算法与仿真 • 上一篇    下一篇

基于相关反馈学习的三维目标检索算法

肖秦琨1,2, 戴琼海1, 尔桂花1, 王洁2   

  1. 1. 清华大学自动化系, 北京, 100084;
    2. 西安工业大学电信学院, 陕西, 西安, 710032
  • 收稿日期:2008-04-03 修回日期:2008-07-18 出版日期:2009-07-20 发布日期:2010-01-03
  • 作者简介:肖秦琨(1974- ),男,副教授,博士后,主要研究方向为动态贝叶斯网络及图像检索.E-mail:xiaoqinkun@tsinghua.edu.cn
  • 基金资助:
    国家自然科学基金(60432030;60525111;60772048);中国博士后科研基金(2008043410);陕西省教育厅科研项目基金(07JK277)资助课题

3D object retrieval algorithm based on the relevance feedback learning

XIAO Qin-kun1,2, DAI Qiong-hai1, ER Gui-hua1, WANG Jie2   

  1. 1. Dept. of Automation, Tsinghua Univ., Beijing 100084, China;
    2. School of Electronic Information Engineering, Xi’an Technological Univ., Xi’an 710032, China
  • Received:2008-04-03 Revised:2008-07-18 Online:2009-07-20 Published:2010-01-03

摘要: 针对三维目标(3D object)检索问题,提出了一种基于混合描述符及多支持向量机融合相关反馈学习的3D目标检索方法。在分析现行3D模型检索方法的基础上,提出了混合描述符HD及相关反馈学习的总体思路。讨论了HD框架构建,即在光场图像阵列自适应聚类基础上,分别实现HD各个子描述符。讨论了基于多支持向量机的融合分类学习机制,并将其用于3D目标检索反馈学习环节,对HD检索性能及相关反馈学习分类进行了实验分析,结果表明所提出的方法是有效的。

Abstract: A novel content-based retrieval algorithm is proposed for meeting 3D object retrieval.Firstly,based on the analysis of shortcomings of existing view-based 3D model retrieval algorithm,so the concept of lightfield hybrid descriptor(denoted as HD) is presented and 3D object retrieval frame based on multi-feature relevance feedback learning is developed.Then overall framework HD,which is made of 3 sub-descriptors,is discussed and how to construct HD is described.Secondly,the 3D object retrieval algorithm based on multiple support vector machines fusion and the relevance feedback learning is used to improve accuracy and effective of 3D object retrieval.In the end,some experiments are done to analyze retrieval performances based on HD and relevance feedback learning.The results show that the proposed method is effective.

中图分类号: