An autoassociator for automatic texture feature extraction

This paper presents an autoassociator neural network for texture feature extraction. Texture features are extracted through the hidden layer of an autoassociator. The Resilient Propagation (RP) algorithm was employed to train the autoassociator with the texture input and output patterns. The perform...

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Bibliographic Details
Main Authors: Kulkarni, S., Verma, B.
Format: Conference Proceeding
Language:English
Subjects:
Australia
Clustering algorithms
Feature extraction
Gabor filters
Gold
Image analysis
Image texture analysis
Information technology
Neural networks
Pixel
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Description
Summary:This paper presents an autoassociator neural network for texture feature extraction. Texture features are extracted through the hidden layer of an autoassociator. The Resilient Propagation (RP) algorithm was employed to train the autoassociator with the texture input and output patterns. The performance of the feature extractor was evaluated on Brodatz benchmark database. A detail analysis of the results is included. The results and analysis showed that the autoassociator is capable of extracting texture features better than the other traditional techniques.
DOI:10.1109/ICCIMA.2001.970488