基于细节层次与最小生成树的三维地形识别与检索1
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Vol.14, No.11
©2003 Journal of Software 软 件 学 报
1000-9825/2003/14(11)1955
基于细节层次与最小生成树的三维地形识别与检索
∗
肖
俊
+
,
庄越挺
,
吴
飞
(浙江大学 人工智能研究所,浙江 杭州 310027)
Recognition and Retrieval of 3D Terrain Based on Level of Detail and Minimum
Spanning Tree
XIAO Jun
+
, ZHUANG Yue-Ting, WU Fei
(Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China)
+ Corresponding author: Phn: 86-571-87951853, Fax: 86-571-87951947, E-mail: xiaojunboy@yeah.net
http://www.cs.zju.edu.cn
Received 2002-07-27; Accepted 2003-01-20
Xiao J, Zhuang YT, Wu F. Recognition and retrieval of 3D terrain based on level of detail and minimum
spanning tree. Journal of Software, 2003,14(11):1955~1963.
http://www.jos.org.cn/1000-9825/14/1955.htm
Abstract: Image, video, audio and graphics are information media in multimedia. In order to use and manage
them effectively, the contents implied by them are needed to analyze. Currently, content-based image (video) and
audio retrieval make some progress. However, there is no a very efficient method to perform a similar graphics
retrieval, especially a similar 3D graphics retrieval. In this paper, an algorithm is presented to implement the similar
3D object recognition and retrieval. In this algorithm, 3D features are first obtained after the meshes of a 3D object
are reduced through level of detail. Since the dimension of the extracted 3D features is very huge, minimum
spanning tree (MST) are used to reduce the features, then the recognition of similar 3D objects are realized by
Support Vector Machine (SVM). The proposed algorithm works well when it is used to recognize and retrieve a 3D
terrain.
Key words: level of detail; minimum spanning tree; support vector machine; 3D retrieval and recognition; feature
reduction
摘 要: 图像、视频、音频和图形等均是多媒体数据流中的信息载体,对上述数据所蕴涵的内容进行分析,可以极
大地方便人们对它们的使用与管理.基于内容的图像(视频)和音频检索已经取得了不少进展,但是对于图形,特别是
3D 图形进行识别与检索的有效方法还很少见.提出了对相似 3D 物体识别与检索的算法,在这个算法中,首先使用细
节层次模型对 3D 物体进行三角面片约减,然后提取 3D 物体的特征.由于所提取的特征维数很大,最小生成树
(minimum spanning tree,简称 MST)被用来对每一个 3D 物体的特征进行约减,基于约减后的特征,实现了基于支持向
∗ Supported by the National Natural Science Foundation of China under Grant No.60272031 (国家自然科学基金); the National
Research Foundation for the Doctoral Program of Higher Education of China No.20010335049 (国家教育部博士点基金); Zhejiang
Provincial Natural Science Foundation of China under Grant No.ZD0212 (浙江省自然科学基金)
第一作者简介: 肖俊(1979－),男,贵州铜仁人,博士生,主要研究领域为机器学习,计算机视觉.
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