Visual attention quality database for benchmarking performance evaluation metrics

Abstract

With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed. These models are evaluated by using performance evaluation metrics that measure how well a predicted map matches eye-tracking data obtained from human observers. Though there are a number of existing performance evaluation metrics, there is no clear consensus on which evaluation metric is the best. This work proposes a subjective study that uses ratings from human observers to evaluate saliency maps computed by existing VA models based on comparing the maps visually with ground-truth maps obtained from eye-tracking data. The subjective ratings are correlated with the scores obtained from existing as well as a proposed objective VA performance evaluation metric using several correlation measures. The correlation results show that the proposed objective VA metric outperforms the existing metrics.