Image Classification Based on Laplacian PCA

摘要：

Feature extraction plays a fundamental role in image classification and retrieval.However,the obtained feature space is often high-dimensional and dimensionality reduction is necessary to alleviate the curse of dimensionality or reduce the computational complexity.In this paper,we propose an image classification approach based on Laplacian PCA(LPCA).The notion of LPCA is borrowed from the area of manifold learning.Compared with the existing method,like PCA or KPCA,the proposed approach is more robustness against noise and weak metric-dependence on sample spaces.Experiments on three real image dataset with use of KNN as the classifier demonstrate the efficiency of the proposed method.

作者: Wengang Cheng De Xu Haibo Wang

作者单位: Dept.of Computer Science,North China Electric Power Univ.,Beijing,China Dept.of Computer Science,Beijing Jiaotong Univ.,Beijing,China

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）