Saliency aggregation via hard-voting evolution

Generic object level saliency detection is important for many vision tasks, such as object detection, compression, recognition, segmentation and so on‥ A variety of saliency detection methods have been proposed in recently, which often complement each other. In order to combine them, we propose a Hard-voting Evolution saliency aggregation algorithm in this paper. Specially, it is consist of three stages. First, we integrate each individual map to get a reference map by linear summation. Second, each of the individual maps is updated in Bayes framework based on the obtained reference map. Third, we use our proposed approach to get the aggregated saliency map. Finally, we do iterative operation to further improve performance. Experiments on three publicly available datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.