Local Non-Negative Matrix Factorization as a Visual Representation

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Venue:
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Year:
2002

Citing 0
Cited 21

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Abstract

In this paper, we propose a novel method, called local Non-negative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual patterns.An objective function is defined to impose localization constraint, in addition t the non-negativity constraint in the standard NMF [1 ].This gives a set of bases which not only allows a non-subtractive (part-based)representation f images but also manifests localized features.An algorithm is presented for the learning of such basiscomponents.Experimental results are presented t compareLNMF with the NMF and PCA methods for face representation and recognition, which demonstrates advantages of LNMF.Based on our LNMF approach, a set of orthogonal, binary,localized basis components are learned from a well aligned face image database.It leads t a Walsh function based representation f the face images.These properties can be used t resolve occlusion problem, improve the computing efficiency, and compress the storage requirement offace detection and recognition system.