Abstract
Non-negative matrix factorization (NMF) technique has been recently proposed for dimensionality reduction. NMF is capable to produce region or part based representations of objects and images. Also, a direct modification of NMF, the weighted non-negative matrix factorization (WNMF) has also been introduced to improve the NMF capabilities of representing positive local data (as color histograms). A comparison between NMF, WNMF and the well-known principal component analysis (PCA) in the context of image patch classification has been carried out and it is claimed that all these three techniques can be combined in a common and unique classifier. This contribution is an extension of a previous study and we introduce the use of the WNMF as well as a probabilistic approach to compare all the three techniques noticing a great improvement in the final recognition results. © 2003 Elsevier B.V. All rights reserved.
Original language | English |
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Pages (from-to) | 2447-2454 |
Journal | Pattern Recognition Letters |
Volume | 24 |
Issue number | 14 |
DOIs | |
Publication status | Published - 1 Jan 2003 |
Keywords
- Color histogram classification
- Non-negative matrix factorization (NMF)
- Principal component analysis (PCA)