基于高斯过程分类器的三维模型多粒度语义检索

3D Model Retrieval with Multi-Granular Semantics Based on Gaussian Process Classifier

为解决三维模型语义检索中用户检索意图不一致问题,建立多粒度语义检索框架,使学习模型能够有效地适应用户的不同检索意图.首先对模型分类知识进行层次划分,形成语义概念的多粒度结构.然后提取一种多视图特征来描述三维模型的形状特性,并采用高斯过程分类器建立不同粒度层次上的学习模型,实现低层特征和查询概念之间的语义一致性描述.和已有研究相比,多粒度语义检索框架使用户可通过语义粒度级别变化进行检索意图设置,从而检索结果尽可能符合用户语义.在实验部分,采用三维模型基准数据库对框架进行算法性能测试.结果表明,检索准确率要明显提高,并且符合人类思维特点.

In order to solve the inconsistency between users＇ intentions in semantic 3 D model retrieval system, a retrieval framework with multi-granular semantics is established, in which learning model can adapt to different user search intentions. Firstly, model classification is divided into different levels and the multi- granularity structure of semantic concept is formed. Then, a hybrid shape feature based on views is used to describe the shape characteristics of 3D model. And the Gaussian process classifier is used to associatelow-level features with query concepts on a different level of semantic concept. Compared with existing research, the retrieval framework with multi-granular semantics allows the users to set their retrieval intentions according to selecting the granular level of semantics, and the results meet the user semantics as much as possible. The experimental results of retrieval performance evaluation using the benchmark show that the retrieval performance using proposed method is significantly higher than content-based retrieval and confident with human concept.