We survey our work on adaptive multiresolution (MR) approaches to the classification of biological images. The system adds MR decomposition in front of a generic classifier consisting of feature computation and classification in each MR subspace, yielding local decisions, which are then combined into a global decision using a weighting algorithm. The system tested on different datasets (subcellular protein location images, drosophila embryo images and histological images images) gave very high accuracies. We hypothesize that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient. Finally, we prove that frames are the class of MR techniques that performs the best in this context. This leads us to consider the construction of a new family of frames for classification, which we term lapped tight frame transforms.