Selection of a best metric and evaluation of bottom-up visual saliency models

There are many “machine vision” models of the visual saliency mechanism, which controls the process of selecting and allocating attention to the most “prominent” locations in the scene and helps humans interact with the visual environment efficiently (Itti and C. Koch, 2001; Gao et al., 2000). It is important to know which models perform the best in mimicking the saliency mechanism of the human visual system. There are several metrics to compare saliency models; however, results from different metrics vary widely in evaluating models. In this paper, a procedure is proposed for evaluating metrics for comparing saliency maps using a database of human fixations on approximately 1000 images. This procedure is then employed to identify the best metric. This best metric is then used to evaluate ten published bottom-up saliency models. An optimized level of the blurriness and center-bias is found for each visual saliency model. Performance of the models is also analyzed on a dataset of 54 synthetic images.