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基于拟Laplace谱的形状表示与聚类 被引量:1

Shape Representation and Clustering Based on Quasi-Laplace Spectrum
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摘要 基于谱图理论的形状表示与聚类是计算机视觉和模式识别领域的重要研究方向。针对不同形状的结构特征,通过对形状骨架点所构完全图的拟Laplace矩阵进行奇异值分解,将得到的高维数据投影至低维空间中,进而分析该数据在低维空间中的分布情况实现聚类。针对公共数据集的对比实验验证了该算法的有效性。 Shape representation and clustering based on spectral graph theory is a hot topic in the field of computer vision and pattern recognition. Aiming at the structure features of different shapes, the high-dimensional data are obtained by means of singular value decomposition on quasi-Laplace matrices of the skeleton of shapes. Furthermore, the shapes are clustered by analyzing the distribution of the projection ina low-dimensional space. The comparative experiments on the public data-set demonstrate the effectiveness of the proposed approach.
出处 《华东理工大学学报(自然科学版)》 CAS CSCD 北大核心 2011年第6期749-753,共5页 Journal of East China University of Science and Technology
基金 国家自然科学基金(11071002) 安徽省教育厅自然科学研究项目(KJ2011A008) 安徽大学211工程学术创新团队项目
关键词 聚类 形状表示 拟Laplace谱 clustering shape representation graph quasi-Laplace spectrum
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参考文献16

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