Image Feature Extraction Using Independent Component Analysis (1996)
| Venue: | In Proc. NORSIG'96 |
| Citations: | 16 - 8 self |
BibTeX
@INPROCEEDINGS{Hurri96imagefeature,
author = {Jarmo Hurri and Aapo Hyvärinen and Juha Karhunen and Erkki Oja},
title = {Image Feature Extraction Using Independent Component Analysis},
booktitle = {In Proc. NORSIG'96},
year = {1996},
pages = {47547--8}
}
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Abstract
In Independent Component Analysis, one tries to model the underlying data so that in the linear expansion of the data vectors the coefficients are as independent as possible. This often leads to natural features characterizing well the data. In this paper, we present some results on applying Independent Component Analysis to image data. This has become possible by using a recently developed, computationally highly efficient fixed-point learning rule. The resulting feature masks are sensitive either to lines and edges of varying thickness or to local spatial features and frequencies.
Citations
| 53 | J.:\Neural Approaches to Independent Component Analysis and Source Separation - Karhunen - 1996 |
| 50 | A fast xed-point algorithm for independent component analysis - Hyvarinen, Oja - 1997 |
| 44 | Generalizations of principal component analysis, optimization problems, and neural networks - Karhunen, Joutsensalo - 1995 |
| 35 | The independent components of natural scenes are edge lters - Bell, Sejnowski - 1997 |
| 18 | A family of ��xed-point algorithms for independent component analysis - Hyvrinen - 1997 |
| 10 | iIndependent component analysis - a new concept?,j - Comon - 1994 |
| 3 | iSignal separation by nonlinear Hebbian learning,j in Computational Intelligence A Dynamic System Perspective - Oja, Karhunen - 1995 |
| 1 | iCharacterization of textures as "eigen��lters",j - Ade - 1983 |
