Integrating Randomization and Discrimination for Classifying Human-Object Interaction Activities

In this chapter we study the problem of classifying human–object interaction activities in still images. The goal of our method is to explore fine image statistics and identify the discriminative image patches for recognition. We achieve this goal by combining two ideas, discriminative feature mining and randomization. Discriminative feature mining allows us to model the detailed information that distinguishes different classes of images, while randomization allows us to handle the huge feature space and prevent over-fitting. We propose a random forest with discriminative decision trees algorithm where every tree node is a discriminative classifier that is trained by combining the information in this node as well as all upstream nodes. Besides human action recognition in still images, we also evaluate our method on subordinate categorization. Experimental results show that our method identifies semantically meaningful visual information and outperforms state-of-the-art algorithms on various datasets. Using our method, we achieved the best results and won the award in PASCAL VOC action classification challenges in 2011 and 2012.