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基于最大相似类别和位置熵的三维模型融合检索方法

  

  • 出版日期:2013-10-31 发布日期:2015-06-19

3D Model Retrieval Based on Maximum Similar Category and Location Entropy

  • Online:2013-10-31 Published:2015-06-19

摘要: 为了有效利用各特征集对三维模型内容的描述信息,对各种特征集上分别
检索的结果进行综合分析,统计各类模型的分布概率得到查询模型的最大相似类别,然后在
各个检索结果中统计该类别模型的位置熵,基于最大相似类别模型数目和位置熵计算融合权
值。在普林斯顿标准3D 模型集上进行实验,并和其他几种动态融合方法和静态方法进行比
较,结果说明所提出的方法在有弱特征集存在的情况下是有效的。

关键词: 3D 模型检索, 多特征, 动态融合

Abstract: For using descriptive information of individual feature of 3D models effectively,
the maximum similar category of the query model is determined by comprehensively analyzing
the distribution of models in retrieved results based on various features. Then the location entropy
of the same class models of individual feature is calculated respectively. Finally, combination
weights are computed dynamically based on the number of models of the maximum similar
category and location entropy. Compared with other dynamic and static combination methods on
Princeton 3D benchmark models, the results show the proposed method is effective in the case of
weak features included.

Key words: 3D model retrieval, multi-features, dynamic combination