A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks
Hierarchical classification of surface defects on dusty wood boards
Pattern Recognition Letters
Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using wavelet transform
Pattern Recognition Letters
Robust Defect Segmentation in Woven Fabrics
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Wavelet methods for texture defect detection
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Expert Systems with Applications: An International Journal
An orthogonal wavelet representation of multivalued images
IEEE Transactions on Image Processing
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
Engineering Applications of Artificial Intelligence
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
A clustering based feature selection method in spectro-temporal domain for speech recognition
Engineering Applications of Artificial Intelligence
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In this paper, a new cluster-based approach is proposed for extracting features from the coefficients of a two-dimensional discrete wavelet transform. The wavelet coefficients from the matrix of each frequency channel are segregated into non-overlapping clusters in an unsupervised mode using a set of application-specific representative images. In practical situations, this set of representative images can be the same as the ones kept aside for training a classifier. The proposed method divides the matrices of computed wavelet coefficients into disjoint clusters that are centered around the position of dominant coefficients. The features that can distinguish images of one class from those of other classes are obtained by computing energies of the clusters. The feature vectors so obtained are then presented as input patterns to an image classifier, such as a neural network. Experimental results based on the applications for texture classification and wood surface defect detection have shown that the proposed cluster-based wavelet feature extraction method is able to effectively extract important intrinsic information content from the test images, and increase the overall classification accuracy as compared with conventional feature extraction methods.