Variational Transform Invariant Mixture of Probabilistic PCA

In many video based object recognition applications, the object appearances are acquired by visual tracking and are inconsistent due to misalignments caused by tracking drifting. We believe the misalignments can be removed if we can reduce the inconsistency in the object appearances caused by misalignments through clustering the objects in appearance, space and time domain simultaneously. We therefore propose to learn transform invariant mixtures of probabilistic PCA (TIMPPCA) model from the data while at the same time eliminating the misalignments. The model is formulated in a generative framework, and the misalignments are considered as hidden variables in the model. Variational EM update rules are then derived based on variational message passing (VMP) techniques. The proposed TIMPPCA is applied to improve head pose estimation performance and to detect the change of attention focus in meeting room video for meeting room video indexing/retrieval and achieved promising performance.