中文摘要：

为解决模型数据集可视化的问题,提出一种基于聚类结果的簇代表模型可视化方案.首先以等距特征映射算法作为模型特征数据的降维方法,将高维特征数据降至三维,并以该三维数据作为簇代表模型的空间位置坐标;然后采用粒子群优化算法得到模型簇的几何中值点,以距几何中值点最近的模型作为该模型簇的代表模型;最后结合模型的对齐方法来确定簇代表模型的姿态,从而实现模型数据集的可视化.另外,根据查询模型与簇代表模型之间的相似性,提出一个基于聚类结果的模型检索流程.该检索流程首先寻找与查询模型最相似的簇代表模型,然后将查询范围限制在这些簇代表模型对应的模型簇中,从而减少备选模型的数量.检索实验结果表明,在合适的参数组合下,该检索流程可以在保证检索精度的同时大幅提高检索效率.

英文摘要：

We present in this paper a clustering-based visualization method for 3D model datasets.Firstly,Isometric feature mapping（Isomap）algorithm is used to reduce high-dimensional data of 3D model to three dimensional data.The reduced data is then used to learn the cluster representative models.Then Particle Swarm Optimization（PSO）is introduced to calculate the geometric median of a model cluster,and the data point which is closest to the geometric median of the cluster is selected as the representative of this cluster.Finally,combining with model alignment approaches,the orientation of the representative model is determined.Furthermore,according to the similarity between a query model and cluster representatives,a process of model retrieval is proposed.The first step of this process is to find the representative models which are most similar to the query model.The search is then restricted within the corresponding clusters which decreases quantity of candidate models.Our experimental results demonstrate that this process can achieve a substantial increase in retrieval efficiency without any loss in retrieval accuracy if an appropriate parameter combination is used.