A Survey of Manifold Learning for Images (bibtex)
by Robert Pless and Richard Souvenir
Abstract:
Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. Understanding this manifold is a key first step in understanding many sets of images, and manifold learning approaches have recently been used within many application domains, including face recognition, medical image segmentation, gait recognition and hand-written character recognition. This paper attempts to characterize the special features of manifold learning on image data sets, and to highlight the value and limitations of these approaches.
Reference:
A Survey of Manifold Learning for Images (Robert Pless and Richard Souvenir), In IPSJ Transactions on Computer Vision and Applications, volume 1, 2009.
Bibtex Entry:
@article{plessSouvenirSurvey2009,
  title={A Survey of Manifold Learning for Images},
  author={Robert Pless and Richard Souvenir},
  journal={IPSJ Transactions on Computer Vision and Applications},
  volume={1},
  pages={83-94},
  year={2009},
  annote={isomap,ML},
  URL={http://www.jstage.jst.go.jp/article/ipsjtcva/1/0/83/_pdf},
  abstract = {Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. Understanding this manifold is a key first step in understanding many sets of images, and manifold learning approaches have recently been used within many application domains, including face recognition, medical image segmentation, gait recognition and hand-written character recognition. This paper attempts to characterize the special features of manifold learning on image data sets, and to highlight the value and limitations of these approaches.}
}
Powered by bibtexbrowser