AdaBoost Learning in Compressed Space for Visual Object Recognition

The CS framework is useful for a much wide range of pattern recognition tasks such as visual object classification. It is possible to directly extract features from a small number of random projections without ever reconstructing the signal, which results in compressed learning. As for compressed learning, it is to learn with randomly projected data, compressed data, instead of original data. Learning with compressed data saves considerable running time and storage since random projection can effectively reduce the dimension of data. In this paper, works dealing with compressed data concentrate on the adaboost classification case. It has been verified by the experiments that the possibility of AdaBoost algorithm learning in compressed space, and a better test error result has been acquired although only the simple stump is adopted as the weak classifiers.