Labeling image patches by boosting based median classifier
This paper presents a median based classifier which predicts the conditional median of the class label given the feature vector. The class label is defined through a hidden variable whose median is further modeled as an additive model of the feature vector. We propose to estimate the conditional median of the hidden variable given the feature vector in the framework of generic functional gradient algorithm . An equivalent form of the definition of median is introduced, whose smoothed version is employed as the objective function. To fit the model for the conditional median, the proposed objective function is maximized by gradient ascent in functional space, updating the fitted model a small step in the gradient direction in each iteration. The resulting algorithm, Median Boost, is a boosting like procedure which obtains the informative features and the classifier at the same time. On the task of labeling building blocks in natural images, the comparison results show that Median Boost performs better than or similar to several alternatives.
Zheng, Songfeng. "Labeling Image Patches by Boosting based Median Classifier." In BMVC, pp. 1-11. 2011.
BMVC 2011 - Proceedings of the British Machine Vision Conference 2011