A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images in the labeled training set. The base classifiers are then learned using features extracted from these randomly transformed versions of the training data, and the result is a highly diverse ensemble of image classifiers. This approach is evaluated on a benchmark pedestrian detection dataset and shown to be effective.