中文摘要：

针对已有多特征描述符因维数增加而导致检索效率明显下降的问题，提出一种三维模型多特征联合的均值描述符．该方法不仅能显著提高检索准确率，而且不会降低检索效率．首先通过调和映射提取三维模型的旋转不变描述符；然后采用多特征描述符对数据库中的模型进行无监督聚类分析，构造能够反映模型聚类性的近邻图；最后根据近邻图对调和描述符进行均值处理，形成特征维数保持不变的均值描述符．对三维模型数据库进行实验分析，并与调和描述符相比较，均值描述符检索准确率明显提高；即使和已有的混合高维描述符相比较，检索准确率也有提高．

英文摘要：

Combining multiple shape descriptors can greatly improve retrieval accuracy. However, it will decrease retrieval efficiency due to the growing feature dimension. This paper addresses this problem, and proposes a new method to combine multiple descriptors, namely mean descriptor. It can greatly improve retrieval accuracy while remaining retrieval efficiency. Firstly, the proposed algorithm uses spherical harmonics to generate harmonics descriptor. Secondly, it constructs nearest graph for models by employing multiple descriptors. Finally, with the above harmonics descriptor and nearest graph, the mean descriptor is generated by meaning operation. The experiments on a public model database benchmark show the retrieval accuracy of mean descriptor has a great improvement over that of harmonics descriptor. Even comparing with the existing multiple shape descriptors, the retrieval accuracy has a small improvement.