Using discriminative analysis for improving hierarchical compositional models

In this paper we propose a method to extract discriminative information from a generative model produced by a compositional hierarchical approach. We present discriminative information as a score computed from a weighted summation of the activation vector. We base the activation vector on individual activations of features from a parse tree of the detection. We utilize the score to reduce false positive detections by removing generative models with poor discriminative information from the vocabulary and by thresholding the detections with low discriminative score. We evaluate our approach on the ETHZ Shape Classes database where we show a reduction in the number of false positives and a decrees in detection time without reducing the detection rate.