I guess this paper deliberates on the trade off between a better feature representation and a better classifier for efficient recognition. Results indicate that the fragment based approach is better than the wavelet approach irrespective of the kind of classifier that is chosen. The authors stress that the performance of wavelet based classifiers could be increased but only at the cost of heavy computations. The authors seem to indicate that a better choice of features has precedence over a good choice of classifiers. However, as Pete indicated the dataset may be insufficient and also wavelets are not the only techniques for generic feature extraction?