基于多特征融合的三维模型检索算法

针对三维模型检索中单一特征检索效果差的难题,首先提出了三维模型的3类特征向量提取算法,即刻画模型表面特性的扩展高斯球面特征向量、反映模型内部结构的Radon变换球面分布特征向量、代表模型投影层次的视图分层压缩感知特征向量。其次,以样本模型的查询结果分类信息熵作为指标并结合监督学习过程,给出了一种多特征融合的加权系数估算方法。最后,设计了融合多特征的模型间相似度度量,完成基于查询示例的模型检索过程。仿真实验表明,提出的3类特征向量具有较好的可区分性,多特征融合检索算法的查全率与查准率有明显提升。

3D Model Retrieval Algorithm Based on Multi Feature Fusion

For the problem of the single feature retrieval effectiveness in 3D model retrieval,in this paper we proposed three kinds of feature vector extraction algorithms of 3D model which include Extended Gauss Sphere(EGS)feature vector of describing the surface characteristics of the model,Radon Transform Spherical Distribution (RTSD) feature vector of reflecting the internal structure of the model,and the View Hierarchical Compressed Sensing (VHCS) feature vector of representing projection layer of the model.Secondly,we presented a weighted coefficient estimation method for multi feature fusion based on the sample model query result classification information entropy and the supervised learning process.Finally,multi feature fusion similarity measure between models was designed to complete the 3D model retrieval based on query sample.Simulation results show that three kinds of feature vectors proposed have better distinguish ability,recall and precision of multi feature fusion retrieval algorithm are improved obviously.