Sorwar, Golam; Abraham, A. and Dooley, Laurence S.
PDF (Not Set)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
|Google Scholar:||Look up in Google Scholar|
We present a classification method based on the discrete cosine transform (DCT) coefficients of texture image. Since the DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images with DCT, we used two popular soft computing techniques, namely neurocomputing and neuro-fuzzy computing. We used a feedforward neural network trained by backpropagation algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. We also analyzed the effects of prolonged training of neural networks. It was observed that the proposed neuro-fuzzy model performed better than neural network.
|Item Type:||Conference Item|
|Extra Information:||ISBN: 0-7803-7293-X|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Laurence Dooley|
|Date Deposited:||30 Sep 2008 12:25|
|Last Modified:||21 Oct 2016 17:29|
|Share this page:|
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.