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.
['Miroslav Kobetski', 'Josephine Sullivan']
Discriminative tree-based feature mapping