Learning Texture Discrimination Rules in a Multiresolution System

H. Greenspan, R. Goodman, C. H. Anderson

Research output: Contribution to journalArticlepeer-review

Abstract

We describe a texture analysis system in which informative discrimination rules are learned from a multiresolution representation of the 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.

Original languageEnglish
Pages (from-to)894-901
Number of pages8
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume16
Issue number9
DOIs
StatePublished - Sep 1994
Externally publishedYes

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