Introducing a weighted non-negative matrix factorization for image classification

D. Guillamet, J. Vitrià, B. Schiele

    Research output: Contribution to journalArticleResearchpeer-review

    145 Citations (Scopus)

    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 languageEnglish
    Pages (from-to)2447-2454
    JournalPattern Recognition Letters
    Volume24
    Issue number14
    DOIs
    Publication statusPublished - 1 Jan 2003

    Keywords

    • Color histogram classification
    • Non-negative matrix factorization (NMF)
    • Principal component analysis (PCA)

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