DocumentCode :
1835481
Title :
Local non-negative matrix factorization as a visual representation
Author :
Feng, Tao ; Li, Stan Z. ; Shum, Heung-Yeung ; Zhang, Hongjiang
Author_Institution :
Microsoft Res. Asia, Beijing Sigma Center, China
fYear :
2002
fDate :
2002
Firstpage :
178
Lastpage :
183
Abstract :
Proposes a novel method, called local non-negative matrix factorization (LNMF), for learning a spatially localized, parts-based subspace representation of visual patterns. An objective function is defined to impose the localization constraint, in addition to the non-negativity constraint in the standard non-negative matrix factorization (NMF). This gives a set of bases which not only allows a non-subtractive (part-based) representation of images but also manifests localized features. An algorithm is presented for the learning of such basis components. Experimental results are presented to compare LNMF with the NMF and principal component analysis (PCA) methods for face representation and recognition, which demonstrates the 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 to a Walsh function-based representation of the face images. These properties can be used to resolve the occlusion problem, improve the computing efficiency and compress the storage requirements of a face detection and recognition system.
Keywords :
Walsh functions; face recognition; image representation; learning (artificial intelligence); matrix decomposition; principal component analysis; spatial reasoning; visual databases; Walsh function-based image representation; computing efficiency; face detection; face recognition; face representation; local nonnegative matrix factorization; localization constraint; localized features; nonnegativity constraint; nonsubtractive image representation; objectivefunction; occlusion; orthogonal binary localized basis components; principal component analysis; spatially localized parts-based subspace representation learning; storage requirement compression; visual patterns; visual representation; well-aligned face image database; Asia; Computer vision; Face detection; Face recognition; Feature extraction; Image analysis; Image coding; Image databases; Pattern analysis; Principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Development and Learning, 2002. Proceedings. The 2nd International Conference on
Print_ISBN :
0-7695-1459-6
Type :
conf
DOI :
10.1109/DEVLRN.2002.1011835
Filename :
1011835
Link To Document :
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