Discriminative tree-based feature mapping

For object classification and detection, the algorithm pipeline often involves classifying feature vectors extracted from image patches. Existing features such as HOG, fail to map the image patches into a space where a linear hyperplane is suitable for separating the classes, while many non-linear classification methods are too expensive for many tasks. We propose a sparse tree-based mapping method that learns a mapping of the feature vector to a space where a linear hyperplane can better separate negative and positive examples. The learned mapping function F(x) results in significant improvement for image patch classification with HOG and LBP-features over other feature mapping methods on VOC2007 and INRIAPerson datasets.