Unsupervised Fisher Vector Adaptation For Re-Identification

Matching and recognizing objects in images and videos, with varying imaging conditions, are a challenging problems. We are particularly interested in the unsupervised setting, i.e., when we do not have labeled data to adapt to the new conditions. Our focus in this work is on the Fisher Vector framework which has been shown to be a state-of-the-art patch encoding technique. Fisher Vectors primarily encode patch statistics by measuring first and second-order statistics with respect to an a priori learned generative model. In this work, we show that it is possible to reduce the domain impact on the Fisher Vector representation by adapting the generative model parameters to the new conditions using unsupervised model adaptation techniques borrowed from the speech community. We explain under which conditions the domain influence is canceled out and show experimentally on two in-house license plate matching databases that the proposed approach improves accuracy.