Nonnegative matrix factorization with Log Gabor wavelets for image representation and classification

作     者：Zheng Zhonglong Yang Jie 

作者机构：Inst. of Image Processing and Pattern Recognition of Shanghai Jiaotong Univ. Shanghai 200030 P. R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2005年第16卷第4期

页      面：738-745页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 

主　　题：non-negative matrix factorization （NMF） Log Gabor wavelets principal component analysis locally linearembedding （LLE） 

摘      要：Many problems in image representation and classification involve some form of dimensionality reduction. Nonnegative matrix factorization （NMF） is a recently proposed unsupervised procedure for learning spatially localized, partsbased subspace representation of objects. An improvement of the classical NMF by combining with Log-Gabor wavelets to enhance its part-based learning ability is presented. The new method with principal component analysis （PCA） and locally linear embedding （LIE） proposed recently in Science are compared. Finally, the new method to several real world datasets and achieve good performance in representation and classification is applied.