A cluster-based wavelet feature extraction method and its application

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.