Training mixture of weighted SVM for object detection using EM algorithm

Abstract

Inspired by the idea of divide-and-conquer approach and discriminatively trained SVM model for object detection, we introduce a method of training mixture of weighted SVM models using EM algorithm. In this paper, we introduce a new part weighted SVM with logistic function to convert its prediction score into pseudo-probability. The part weight is computed by an energy estimation method to reflect the discriminative power of different object parts, and the conversion of prediction score to probability enables the input to be assigned to a proper SVM based on unbiased prediction scores among multiple SVM models. More importantly, the two modifications fit the joint training process of multiple SVMs into the EM framework, where we could iteratively reassign the object examples into different sub-regions of the entire input space, and then retrain the SVM models corresponding to that sub-region. In this way, the mixture of SVM models becomes a set of experts to form the mixture of DPMs. Experimental results show that our proposed method made noticeable improvements over the baseline method, which demonstrates the advantage of our proposed method for training MDPM based models for object detection. HighlightsWe introduce a method to balance the training data of multiple SVM classifiers.We propose to reweight the part models according to their discriminating power.We jointly optimize the mixture of DPMs under the EM framework.