New paper: Unifying saliency metrics


How well can we predict where people will look in an image? A large variety of models have been proposed that try to predict where people look using only the information provided by the image itself. The MIT Saliency Benchmark, for example, compares 47 models.

So which model is doing the best? Well, it depends which metric you use to compare them. That particular benchmark lists 7 metrics; ordering by a new one changes the model rankings. That’s a bit confusing. Ideally we would find a metric that unambiguously tells us what we want to know.

Over a year ago, I wrote this blog post telling you about our preprint How close are we to understanding image-based saliency?. After reflecting on the work, we realised that the most useful contribution of that paper was buried in the appendix (Figure 10). Specifically, putting the models onto a common probabalistic scale makes all the metrics* agree. It also allows the model performance per se to be separated from nuisance factors like centre bias and spatial precision, and for model predictions to be evaluated within individual images.

We re-wrote the paper to highlight this contribution, and it’s now available hereThe code is available here.


Kümmerer, M., Wallis, T.S.A. and Bethge, M. (2015). Information-theoretic model comparison unifies saliency metrics. Proceedings of the National Academy of Sciences.

*all the metrics we evaluated, at least.

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