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	图像数据库的保局聚类
	郑欣 ; 林学訚 ; Zheng Xin ; Lin Xueyin
	2010-06-09 ; 2010-06-09
关键词	聚类 谱聚类 图像聚类 保局聚类 保局投影 clustering spectral clustering image clustering locality preserving clustering (LPC) locality preserving projections (LPP) TP311.13
其他题名	Locality Preserving Clustering for Image Database
中文摘要	面对日益增长的图像数据库,为用户提供一个简洁高效的搜索和浏览解决方案成为一个紧迫而且充满挑战的问题.图像聚类技术可以在许多方面为此提供帮助,例如图像数据预处理、用户界面设计,以及对搜索结果的聚类等.在众多聚类算法中,谱聚类(spectral clustering)方法由于能够解决复杂分布数据的聚类问题,以及接近全局最优的性能,成为近年来广受关注的一种方法.然而,目前存在的谱聚类方法,譬如normalized cut在处理新增数据点的聚类时,计算复杂度很高.提出了一种新的聚类算法——保局聚类.保局聚类在拥有许多非线性谱聚类方法优点的同时,又具有独特的数学特性——能提供显式的映射函数.这为在原数据集和新增数据集上进行高效的聚类提供了可能.实验结果显示,保局聚类比K均值聚类和主成分分析后的K均值聚类效果要好.实验同样显示,保局聚类与normalized cut效果可比,而前者更加高效.; It is important and challenging to make the growing image repositories easy to search and browse. Image clustering is a technique that helps in several ways, including image data preprocessing, the user interface design, and search result representation. Spectral clustering method has been one of the most promising clustering methods in the last few years, because it can cluster data with complex structure, and the (nearly) global optimum is guaranteed. However, the existing spectral clustering algorithms, like normalized cut (NCut), are difficult to use to handle data points out of training set. In this paper, a clustering algorithm named LPC (locality preserving clustering) is proposed, which shares many of the data representation properties of nonlinear spectral method. Yet the LPC provides an explicit mapping function, which is defined everywhere, on both training data points and testing points. Experimental results show that LPC is more accurate than both “direct Kmeans” and “PCA+Kmeans”. It is also shown that LPC produces comparable results with NCut, yet is more efficient than NCut.; 国家“九七三”重点基础研究发展规划基金项目(2002CB312101); 国家自然科学基金项目(60433030)~~
语种	中文 ; 中文
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/56137]
专题	清华大学
推荐引用方式 GB/T 7714	郑欣,林学訚,Zheng Xin,等. 图像数据库的保局聚类[J],2010, 2010.
APA	郑欣,林学訚,Zheng Xin,&Lin Xueyin.(2010).图像数据库的保局聚类..
MLA	郑欣,et al."图像数据库的保局聚类".(2010).

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