Exploiting Feature Correlations by Brownian Statistics for People Detection and Recognition
Characterizing an image region by its feature intercorrelations is a modern trend in computer vision. In this paper, we introduce a new image descriptor that can be seen as a natural extension of a standard covariance descriptor with the advantage of capturing nonlinear and nonmonotone dependencies. Inspired from the recent advances in mathematical statistics of Brownian motion, we can express highly complex structural information in a compact and computationally efficient manner. We show that our Brownian covariance descriptor can capture richer image characteristics than the covariance descriptor. Additionally, a detailed analysis of the Brownian manifold reveals that opposite to the classical covariance descriptor, the proposed descriptor lies in a relatively flat manifold, which can be treated as a Euclidean. This brings significant boost in the efficiency of the descriptor. The effectiveness and the generality of our approach is validated on two challenging vision tasks, pedestrian classification, and person reidentification. The experiments are carried out on multiple datasets achieving promising results.