Name
Title | ||
|---|---|---|
Projective Non-Negative Matrix Factorization With Applications To Facial Image Processing |
Abstract | ||
|---|---|---|
We propose a new variant of Non-negative Matrix Factorization (NMF), including its model and two optimization rules. Our method is based on positively constrained projections and is related to the conventional SVD or PCA decomposition. The new model can potentially be applied to image compression and feature extraction problems. Of the latter, we consider processing of facial images, where each image consists of several parts and for each part the observations with different lighting mainly distribute along a straight line through the origin. No regularization terms are required in the objective functions and both suggested optimization rules can easily be implemented by matrix manipulations. The experiments show that the derived base vectors are spatially more localized than those of NMF. In turn, the better part-based representations improve the recognition rate of semantic classes such as the gender or existence of mustache in the facial images. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1142/S0218001407005983 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
non-negative matrix factorization, projective, facial image, principal component analysis | Singular value decomposition,Line (geometry),Pattern recognition,Matrix (mathematics),Matrix decomposition,Image processing,Feature extraction,Non-negative matrix factorization,Artificial intelligence,Machine learning,Mathematics,Image compression | Journal |
Volume | Issue | ISSN |
21 | 8 | 0218-0014 |
Citations | PageRank | References |
20 | 0.90 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhirong Yang | 1 | 289 | 17.27 |
| Zhijian Yuan | 2 | 220 | 12.17 |
| Jorma Laaksonen | 3 | 1162 | 176.93 |