Learning texture discrimination rules in a multiresolution system

We describe a texture analysis system in which informative discrimination rules are learned from a multiresolution representation of time textured input. The system incorporates unsupervised and supervised learning via statistical machine learning and rule-based neural networks, respectively. The te...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 16; no. 9; pp. 894 - 901
Main Authors Greenspan, H., Goodman, R., Chellappa, R., Anderson, C.H.
Format Journal Article
LanguageEnglish
Published IEEE 01.09.1994
Subjects
Feature extraction
Frequency
Labeling
Laboratories
Libraries
Machine learning
Neural networks
Propulsion
Space technology
Supervised learning
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Summary:We describe a texture analysis system in which informative discrimination rules are learned from a multiresolution representation of time textured input. The system incorporates unsupervised and supervised learning via statistical machine learning and rule-based neural networks, respectively. The textured input is represented in the frequency-orientation space via a log-Gabor pyramidal decomposition. In the unsupervised learning stage a statistical clustering scheme is used for the quantization of the feature-vector attributes. A supervised stage follows in which labeling of the textured map is achieved using a rule-based network. Simulation results for the texture classification task are given. An application of the system to real-world problems is demonstrated.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0162-8828
1939-3539
DOI:10.1109/34.310685