一种基于潜在语义线性嵌入的三维模型检索方法

3D Model Retrieval Based on Latent Semantic Linear Embedding

在三维模型检索中,为了保持模型之间的形状相似性,并且考虑保持模型之间的语义关系不变的情况下实现特征的维数缩减,提出一个基于潜在语义分析的非线性降维方法-潜在语义线性嵌入LSLE.该方法通过构建的三维模型语义空间确定数据库中三维模型的语义近邻,并通过改进的局部线性嵌入方法实现数据维数的缩减,对Princeton Shape Benchmark的实验表明,该方法在三维模型的特征降维和检索中都取得了较好的效果.

In the field of 3D model retrieval,in order to holding the shape similarity and the semantic relationship of the 3D models during the course of feature dimension reduction,the paper explores a LSA based non-liner method to reduce 3D model features′ dimension.We can get all the 3D models′ semantic neighbors by constructing a semantic space and reduce the data dimension by improved Locally Linear Embedding method.The experiments on Princeton Shape Benchmark show that the proposed method achieves good performance not only in reducing dimension but also in retrieval.