中文摘要：

特征组合是提高三维模型检索有效性的一种重要手段,为了能更有效地引导特征组合,提出一种借助检索有效性单值评价指标来进行特征组合的方法.该方法采用了深度图、视图特征集、法向量信息熵和射线4种特征,首先对训练集分别计算这4种特征的检索有效性单值评价指标,并依据这些评价指标来确定特征距离的权重;然后在对测试集的检索中,使用权重来组合根据单一特征得到的特征距离,以度量三维模型的相似性.实验结果表明,文中方法的检索有效性优于经典的DESIRE特征组合方法.

英文摘要：

Feature combination is an important way to improve the effectiveness of 3D model retrieval. In order to conduct combination more effectively, we propose a novel approach to combine features using quantitative evaluation indicators of the retrieval performance, which adopts depth buffer images, view feature set, normal entropies, and ray-based feature. Firstly, from the training set, quantitative evaluation indicators of the four features are computed which are then used to compute the weights of feature distances. Secondly, for testing set, the weights are used to combine feature distances computed using every single feature. Finally, the similarities between 3D models can be measured. Experimental results show that our method outperforms the traditional DESIRE approach.