NMF with loggabor wavelets for visualization

Authors:
Zhonglong Zheng;Jianmin Zhao;Jie Yang
Affiliations:
Institute of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;Institute of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
Venue:
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Year:
2005

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Abstract

Many problems in image representation and classification involve some form of dimensionality reduction. Non-negative matrix factorization (NMF) is a recently proposed unsupervised procedure for learning spatially localized, parts-based subspace representation of objects. Here we present an improvement of the classical NMF by combining with Log-Gabor wavelets to enhance its part-based learning ability. In addition, we compare the new method with principal component analysis (PCA) and locally linear embedding (LLE) proposed recently in Science. Finally, we apply the new method to several real world datasets and achieve good performance in representation and classification.