Texture analysis via unsupervised and supervised learning

H. Greenspan*, R. Goodman, R. Chellappa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations


A framework for texture analysis based on combined unsupervised and supervised learning is proposed. The textured input is represented in the frequency-orientation space via a Gabor-wavelet pyramidal decomposition. In an unsupervised learning phase a neural network vector quantization scheme is used for the quantization of the feature-vector attributes and a projection onto a reduced dimension clustered map for initial segmentation. A supervised stage follows in which labeling of the textured map is achieved using a rule-based system. A set of informative features are extracted in the supervised stage as congruency rules between attributes using an information-theoretic measure. This learned set can now act as a classification set for test images. This approach is suggested as a general framework for pattern classification. Simulation results for the texture classification are given.

Original languageEnglish
Title of host publicationProceedings. IJCNN-91-Seattle
Subtitle of host publicationInternational Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Number of pages6
ISBN (Print)0780301641
StatePublished - 1991
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: 8 Jul 199112 Jul 1991

Publication series

NameProceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks


ConferenceInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA


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