Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories

Michele Fenzi, Laura Leal-Taixe, Bodo Rosenhahn, Jorn Ostermann; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 755-762

In this paper, we propose a method for learning a class representation that can return a continuous value for the pose of an unknown class instance using only 2D data and weak 3D labelling information. Our method is based on generative feature models, i.e., regression functions learnt from local descriptors of the same patch collected under different viewpoints. The individual generative models are then clustered in order to create class generative models which form the class representation. At run-time, the pose of the query image is estimated in a maximum a posteriori fashion by combining the regression functions belonging to the matching clusters. We evaluate our approach on the EPFL car dataset [17] and the Pointing'04 face dataset [8]. Experimental results show that our method outperforms by 10% the state-of-the-art in the first dataset and by 9% in the second.