Online Recognition via a Finite Mixture of Multivariate Generalized Gaussian Distributions

The huge amount of data expanding day by day entail creating powerful real-time algorithms. Such algorithms allow a reactive processing between the input multimedia data and the system. In particular, we are mainly concerned with active learning and clustering images and videos for the purpose of pattern recognition. In this paper, we propose a novel online recognition algorithm based on multivariate generalized Gaussian distributions. We estimate at first the generative model’s parameters within a discriminative framework (fixed-point, Riemannian averaged fixed-point, and Fisher scoring). Then, we propose an online recognition algorithm in accordance with those algorithms. Finally, we applied our proposed framework on three challenging problems, namely: human action recognition, facial expression recognition, and pedestrian detection from infrared images. Experiments demonstrate the robustness of our approach by comparing with the state-of-the art algorithms and offline learning techniques.