In this paper we identify two types of problems with excessive feature sharing and the lack of discriminative learning in hierarchical compositional models: (a) similar category misclassifications and (b) phantom detections in background objects. We propose to overcome those issues by fully utilizing a discriminative features already present in the generative models of hierarchical compositions. We introduce descriptor called histogram of compositions to capture the information important for improving discriminative power and use it with a classifier to learn distinctive features important for successful discrimination. The generative model of hierarchical compositions is combined with the discriminative descriptor by performing hypothesis verification of detections produced by the hierarchical compositional model. We evaluate proposed descriptor on five datasets and show to improve the misclassification rate between similar categories as well as the misclassification rate of phantom detections on backgrounds. Additionally, we compare our approach against a state-of-the-art convolutional neural network and show to outperform it under significant occlusions. 
Adding discriminative power to a generative hierarchical compositional model using histograms of compositions