A Statistical Method for Selecting Pattern Descriptors of Textured 3D Models
A Statistical Method for Selecting Pattern Descriptors of Textured 3D Models
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发表时间：
2010-10
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通讯作者：
Motofumi T. Suzuki;Y. Yaginuma;Haruo Kodama
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作者：
Motofumi T. Suzuki;Y. Yaginuma;Haruo Kodama
本文描述了一种针对具有实体纹理的 3D 模型的相似性检索技术。从数据库中的每个 3D 模型中提取了基于三维扩展分形的描述符。使用不同尺寸的分形滤波器来提取 3D 模型的描述符，并将描述符作为 3D 模型的索引进行比较。由于系统中使用的过滤器大小会影响检索性能，因此确定正确的大小非常重要。在我们的实验中，数据库的部分被标记为学习数据集，并且使用学习数据集通过多元回归分析来分析描述符和过滤器大小之间的关系。我们的实验系统反映了在每个查询中选择最佳过滤器大小以最大化相似性检索性能的分析结果。该初步实验检索系统已被实现用于搜索具有实体纹理图案的类似 3D 模型。我们的方法的实验结果表明，在召回率和精确率方面，检索性能有所提高。
This paper describes a similarity re- trieval technique for 3D models with solid textures. Three dimensionally extended fractal based descrip- tors have been extracted from each 3D model in a database. Various sizes of fractal filters were used for extracting descriptors of the 3D models, and the descriptors were compared as indices of the 3D mod- els. Since the filter size used in the system affects retrieval performances, it is important to determine the correct size. In our experiment, portions of the database were marked as a learning data set, and the relationship between the descriptors and filter sizes was analyzed by multiple regression analysis using the learning data set. Our experimental system reflects the analysis results for choosing optimal filter size at each query for maximizing similarity retrieval perfor- mance. This preliminary experimental retrieval sys- tem has been implemented for searching for similar 3D models with solid texture patterns. The experi- mental results of our approach showed retrieval per- formance improvements in terms of recall-precision rates.