Validating the Visual Saliency Model

Bottom up attention models suggest that human eye movements can be predicted by means of algorithms that calculate the difference between a region and its surround at different image scales where it is suggested that the more different a region is from its surround the more salient it is and hence the more it will attract fixations. Recent studies have however demonstrated that a dummy classifier which assigns more weight to the center region of the image out performs the best saliency algorithm calling into doubt the validity of the saliency algorithms and their associated bottom up attention models. In this paper, we performed an experiment using linear discrimination analysis to try to separate between the values obtained from the saliency algorithm for regions that have been fixated and others that haven’t. Our working hypothesis was that being able to separate the regions would constitute a proof as to the validity of the saliency model. Our results show that the saliency model performs well in predicting non-salient regions and highly salient regions but that it performs no better than a random classifier in the middle range of saliency.

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