图学学报
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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
陈俊英, 孟月波, 王羡慧, 刘四妹. 基于最大相似类别和位置熵的三维模型融合检索方法[J]. 图学学报.
Chen Junying, Meng Yuebo, Wang Xianhui, Liu Simei. 3D Model Retrieval Based on Maximum Similar Category and Location Entropy[J]. Journal of Graphics.
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http://www.txxb.com.cn/CN/Y2013/V34/I5/51