Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2594
Title: Subband effect of the wavelet fuzzy C-means features in texture classification
Authors: Dagher, Issam 
Issa, Saad
Affiliations: Department of Computer Engineering 
Keywords: Texture classification
Wavelet transform
FCM
Subbands
K-nearest neighbors
Issue Date: 2012
Part of: Journal of image and vision computing
Volume: 30
Issue: 11
Start page: 896
End page: 905
Abstract: 
The wavelet transform is an important analysis used in the field of texture classification. It decomposes an image into subbands. Some of the subbands contain more significant coefficients than others. Based on this property, we propose a texture analysis and classification approach using a combination of the fuzzy C-means clustering method (FCM) and the wavelet transform. By taking the energy coefficients of two pairs of frequency channels resulting from 2D wavelet transform, and grouping the data into a specific number of clusters, we were able to build a feature list for each texture. The feature list is obtained by applying the FCM on each frequency channel pair. The centers obtained are used as the features for every combination of frequency channel pair; the partition matrix generated from the FCM is used as a method for determining the k-nearest neighbors of an unknown texture. The subband effect of the wavelet FCM features is studied by varying the number of decomposition levels of the wavelet tree. Optimal number of features was obtained by varying the number of clusters and the k-nearest neighbors of the FCM. Experiments show that this method outperformed other methods (linear regression model, Gabor transform). Highlights ► Texture classification is performed using FCM (fuzzy C-means algorithm) based on wavelet transform with optimized number of features. ► The subband effect of the wavelet FCM features are studied by varying the number of decomposition levels of the wavelet tree. ► Optimal number of features was obtained by varying the number of clusters and the k-nearest neighbors of the FCM. ► Experiments show that this method outperformed other methods (linear regression model, Gabor transform).
URI: https://scholarhub.balamand.edu.lb/handle/uob/2594
DOI: 10.1016/j.imavis.2012.07.007
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Computer Engineering

Show full item record

SCOPUSTM   
Citations

9
checked on Jul 31, 2021

Record view(s)

1
checked on Jul 31, 2021

Google ScholarTM

Check

Dimensions Altmetric

Dimensions Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.