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Texture analysis via unsupervised and supervised learning

Greenspan, H. and Goodman, R. and Chellappa, R. (1991) Texture analysis via unsupervised and supervised learning. In: IJCNN-91-Seattle International Joint Conference on Neural Networks. Vol.1. IEEE , Piscataway, NJ, pp. 639-644. ISBN 0780301641. https://resolver.caltech.edu/CaltechAUTHORS:20190314-142000852

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Abstract

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 the 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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ijcnn.1991.155254DOIArticle
Additional Information:© 1991 IEEE. This work is funded in part by DARPA under the grant AFOSR-90-0199 and in part by the Army Research Office under the contract DAAL03-89-K-0126. Part of this work was done at Jet Propulsion Laboratory. The advice and software support of the image-analysis group there, especially that of Charlie Anderson, is greatly appreciated.
Funders:
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Air Force Office of Scientific Research (AFOSR)AFOSR-90-0199
Army Research Office (ARO)DAAL03-89-K-0126
Record Number:CaltechAUTHORS:20190314-142000852
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190314-142000852
Official Citation:H. Greenspan, R. Goodman and R. Chellappa, "Texture analysis via unsupervised and supervised learning," IJCNN-91-Seattle International Joint Conference on Neural Networks, Seattle, WA, USA, 1991, pp. 639-644 vol.1. doi: 10.1109/IJCNN.1991.155254
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93837
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:14 Mar 2019 22:00
Last Modified:03 Oct 2019 20:58

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