  The human visual system employs a selective attention mechanism to understand
the visual world in an eficient manner. In this paper, we show how
computational models of this mechanism can be exploited for the computer vision
application of scene recognition. First, we consider saliency weighting and
saliency pruning, and provide a comparison of the performance of different
attention models in these approaches in terms of classification accuracy.
Pruning can achieve a high degree of computational savings without
significantly sacrificing classification accuracy. In saliency weighting,
however, we found that classification performance does not improve. In
addition, we present a new method to incorporate salient and non-salient
regions for improved classification accuracy. We treat the salient and
non-salient regions separately and combine them using Multiple Kernel Learning.
We evaluate our approach using the UIUC sports dataset and find that with a
small training size, our method improves upon the classification accuracy of
the baseline bag of features approach.
