In the past decades, hundreds of saliency models have been proposed for fixation prediction, along with dozens of evaluation metrics. However, existing metrics, which are often heuristically designed, may draw conflict conclusions in comparing saliency models. As a consequence, it becomes somehow confusing on the selection of metrics in comparing new models with state-of-the-arts. To address this problem, we propose a data-driven metric for comprehensive evaluation of saliency models. Instead of heuristically designing such a metric, we first conduct extensive subjective tests to find how saliency maps are assessed by the human-being. Based on the user data collected in the tests, nine representative evaluation metrics are directly compared by quantizing their performances in assessing saliency maps. Moreover, we propose to learn a data-driven metric by using Convolutional Neural Network. Compared with existing metrics, experimental results show that the data-driven metric performs the most consistently with the human-being in evaluating saliency maps as well as saliency models.
['Jia Li', 'Changqun Xia', 'Yafei Song', 'Shu Fang', 'Xiaowu Chen']
A Data-Driven Metric for Comprehensive Evaluation of Saliency Models