Introducing a weighted non-negative matrix factorization for image classification

doi:10.1016/S0167-8655(03)00089-8

Return

Introducing a weighted non-negative matrix factorization for image classification

2003-10-01

161

PRE

Jordi Vitrià

Bernt Schiele

DOI：10.1016/S0167-8655(03)00089-8

Original

Original request for help

Save

Abstract

En 中文

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. (C) 2003 Elsevier B.V. All rights reserved.

Keywords:

non-negative matrix factorization (NMF)

principal component analysis (PCA)

color histogram classification

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.