@inproceedings{170a0433453c40ca8c22af60679eed8e,
title = "Texture analysis via unsupervised and supervised learning",
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 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.",
author = "H. Greenspan and R. Goodman and R. Chellappa",
year = "1991",
language = "אנגלית",
isbn = "0780301641",
series = "Proceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks",
publisher = "Publ by IEEE",
pages = "639--644",
editor = "Anon",
booktitle = "Proceedings. IJCNN-91-Seattle",
note = "International Joint Conference on Neural Networks - IJCNN-91-Seattle ; Conference date: 08-07-1991 Through 12-07-1991",
}