PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning High-level Independent Components of Images through a Spectral Representation
Jussi Lindgren and Aapo Hyvärinen
In: ICPR 2004, 23-26 Aug 2004, Cambridge, UK.

Abstract

Statistical methods, such as independent component analysis, have been successful in learning local low-level features from natural image data. Here we extend these methods for learning high-level representations of whole images or scenes. We show empirically that independent component analysis is able to capture some intuitive natural image categories when applied on histograms of outputs of ordinary Gabor-like filters. This can be taken as an indication that maximizing the independence or sparseness of features may be a meaningful strategy even on higher levels of image processing, for such advanced functionality as object recognition or image retrieval from databases.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
ID Code:156
Deposited By:Jussi Lindgren
Deposited On:01 June 2004