@inproceedings{leborgne2001ica,
  title     = {Sparse-dispersed coding and images discrimination with independent component analysis},
  author    = {Le Borgne, Herv{\'e} and Gu{\'e}rin-Dugu{\'e}},
  booktitle = {International Conference on ICA and BSS},
  location  = {San Diego, California, USA},
  year      = {2001},
  url_PDF   = {https://hleborgne.github.io/files/hlb2001ica.pdf},
  abstract  = {Independent Component Analysis applied to a set of natural images provides band-pass-oriented filters, similar to simple cells of the primary visual cortex. We applied two types of pre-processing to the images, a low-pass and a whitening one in a multiresolution grid, and examine the properties of the detectors extracted by ICA. These detectors composed a new basis function set in which images are encoded. On one hand, the properties (sparseness and dispersal) of the resulting coding are compared for both pre-processing strategies. On the other hand, this new coding by independent features is used for discriminating natural images, that is a very challenging domain in image analysis and retrieval. We show that a criterion based on the dispersal property enhances the efficiency of the discrimination by selecting the most dispersed detectors coding the image database. This behaviour is well enhanced with whitened images.},
  keywords  = {ica-image}
}

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