基于相关反馈学习的三维目标检索算法

Abstract

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 H_D) is presented and 3D object retrieval frame based on multi-feature relevance feedback learning is developed.Then overall framework H_D,which is made of 3 sub-descriptors,is discussed and how to construct H_D 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 H_D and relevance feedback learning.The results show that the proposed method is effective.

CLC Number:

TP18

XIAO Qin-kun, DAI Qiong-hai, ER Gui-hua, WANG Jie. 3D object retrieval algorithm based on the relevance feedback learning[J]. Journal of Systems Engineering and Electronics, 2009, 31(7): 1733-1737.

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3D object retrieval algorithm based on the relevance feedback learning

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