A robust metric for the evaluation of visual saliency models
Puneet Kumar Sharma, Ali Alsam · 2014
Finding a robust metric for evaluating the visual saliency algorithms has been the subject of research for decades. Motivated by the shuffled AUC metric in this paper, we propose a robust AUC metric that uses the statistical analysis of the fixations data to better judge the goodness of the different saliency algorithms. To calculate the robust AUC metric, we use the first eigenvector obtained from the statistical analysis to define the area from which non-fixations are selected thus mitigating the effect of the center bias. Our results show that the proposed metric results in similar performance when compared with the shuffled AUC metric, but given that the proposed metric is derived from the statistics for the data set, we believe that it is more robust.