ML Anthology

Learning Spatially Localized, Parts-Based Representation

Stan Z. Li, Xinwen Hou, HongJiang Zhang, QianSheng Cheng
CVPR 2001 pp. I:207-212
doi:10.1109/CVPR.2001.990477 /cvpr/2001/li2001cvpr-learning/

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 a localization constraint, in addition to the non-negativity constraint in the standard 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 basic components. Experimental results are presented to compare LNMF with the NMF and PCA methods for face representation and recognition, which demonstrates advantages of LNMF.

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Cite

Text

Li et al. "Learning Spatially Localized, Parts-Based Representation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990477

Markdown

[Li et al. "Learning Spatially Localized, Parts-Based Representation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/li2001cvpr-learning/) doi:10.1109/CVPR.2001.990477

BibTeX

@inproceedings{li2001cvpr-learning,
  title     = {{Learning Spatially Localized, Parts-Based Representation}},
  author    = {Li, Stan Z. and Hou, Xinwen and Zhang, HongJiang and Cheng, QianSheng},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2001},
  pages     = {I:207-212},
  doi       = {10.1109/CVPR.2001.990477},
  url       = {https://mlanthology.org/cvpr/2001/li2001cvpr-learning/}
}