Log-Linear Mixtures for Object Class Recognition
Tobias Weyand, Thomas Deselaers, Hermann Ney · 2009
We present log-linear mixture models as a fully discriminative approach to object cate-gory recognition which can, analogously to kernelised models, represent non-linear de-cision boundaries. We show that this model is the discriminative counterpart to Gaussian mixtures and that either one can be transformed into the respective other. However, the proposed model is easier to extend toward fusing multiple cues and numerically more stable to train and to evaluate. Experiments on the PASCAL VOC 2006 data show that the performance of our model compares favourably well to the state-of-the-art despite the model consisting of an order of magnitude fewer parameters, which suggests excellent generalisation capabilities. 1