Saliency Integration: An Arbitrator Model
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Saliency integration approaches have aroused general concern on unifying saliency maps from multiple saliency models. In fact, saliency integration is a weighted aggregation of multiple saliency maps, such that measuring the weights of saliency models is essential. In this paper, we propose an unsupervised model for saliency integration, namely the arbitrator model (AM), based on the Bayes' probability theory. The proposed AM incorporates saliency models of varying expertise and a prior map based on the consistency of the evidence from multiple saliency models and a reference saliency map from generally accepted knowledge. Also, we suggest two methods to learn the expertise of the saliency models without ground truth. The experimental results are from various combinations of twenty-two state-of-the-art saliency models on five datasets. The evaluation results show that the AM model improves the performance substantially compared to the existing state-of-the-art approaches, regardless of the chosen candidate saliency models.
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