Boosting has emerged as a popular combination technique to refine weak classifiers. Pioneered by Freund and Schapire, numerous variations of the AdaBoost algorithm have emerged, such as Breiman's arc-fs algorithms. The central theme of these methods is the generation of an ensemble of a weak learning algorithm using modified versions of the original training set, with emphasis placed on the more difficult instances. The validation stage then aggregates results from each element of the ensemble using some predetermined rule. In this paper the wavelet decomposition based codebook classifier proposed by Chan et al. is used as the learning algorithm. Starting with the whole training set, modifications to the training set are made at each iteration by re-sampling the original training data set with replacement. The weights used in the re-sampling are determined using different algorithms, including AdaBoost and arc-fs. The accuracies of the ensembles generated are then determined using various combination techniques such as simple voting and weighted sums. Boosting improves upon the two classifier methods (K-means and LVQ) by exploiting their inherent codebook nature.