How Important are the Sizes and Locations of Fixation Regions for the BIAS Model?

In this work we consider the Bayesian integrate and shift (BIAS) model for learning object categories and investigate its sensitivity to changes in the sizes and locations of fixation regions. We test the model using a face category and show that the learning algorithm is robust to large variations of the regions´ sizes and locations. Specifically, we show that the performance is inversely proportional to the sizes of the fixation regions and that the preferred locations are those that are closer to the center of the object.