Texture analysis via unsupervised and supervised learning
- 9 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. i, 639-644 vol.1
- https://doi.org/10.1109/ijcnn.1991.155254
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.Keywords
This publication has 7 references indexed in Scilit:
- Multichannel texture analysis using localized spatial filtersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990
- A Rule-Based Approach to Neural Network ClassifiersPublished by Springer Nature ,1990
- THE INDUCTION OF PROBABILISTIC RULE SETS– THE ITRULE ALGORITHMPublished by Elsevier ,1989
- The generalized Gabor scheme of image representation in biological and machine visionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988
- Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filtersJournal of the Optical Society of America A, 1985
- The Laplacian Pyramid as a Compact Image CodeIEEE Transactions on Communications, 1983
- Textons, the elements of texture perception, and their interactionsNature, 1981