Bayesian optimization of the scale saliency filter

The scale saliency feature extraction algorithm by Kadir and Brady has been widely used in many computer vision applications. However, when compared to other feature extractors, its computational cost is high. In this paper, we analyze how saliency evolves through scale space, demonstrating an intuitive idea: if an image region is homogeneous at higher scales, it will probably also be homogeneous at lower scales. From the results of this analysis we propose a Bayesian filter based on Information Theory, that given some statistical knowledge about the images being considered, discards pixels from an image before applying the scale saliency detector. Experiments show that if our filter is used, the efficiency of the original algorithm increases with low localization and detection error.