Abstract：To enhance the computational efficiency of saliency model, a continuous region feature and background learning based model is proposed and the salient regions of images are extracted and fused. Firstly, the distance from the target pixel to the pixels of the region around is calculated, and a method for measuring the comparison of saliency based on Bayesian is proposed. The continuity of regions are merged. The void regions are merged with the most similar regions to themselves. Then, three typical saliency algorithms are employed to deal with the same image, and consequently different saliency maps are obtained. The background of each significant feature map is acquired in the contrast way, and the mixed Gauss background model is established. The background maps are obtained by learning weight coefficient, and then the saliency regions are acquired through subtracting background map from the image. Finally, the saliency regions are fused with the cell regulation. The proposed algorithm is validated on public SED1 and ASD datasets. The F-Measure and MAE of the proposed algorithm are superior to those of the current popular algorithms.
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2018, Vol. 31 
