In this paper, a multilayer graph-based saliency detection algorithm by merging fixation is proposed to effectively detect salient objects in complex scenes. First, the fixation location of an image is acquired by using fixation prediction models. This is based on a motivation that human visual attention system would quickly focus on salient regions before further processing. Then, by merging the fixation saliency map into an over segmented region map, we can obtain a coarse detection result which most likely contains salient objects. To further improve the performance of our saliency detection, the next key idea is to leverage color contrast between superpixels as features in CIE-Lab space and resolve saliency estimation of coarse regions via a multilayer graph-based framework. The final saliency detection is achieved by combining the coarse detection result with multilayer saliency maps. Extensive experiments are conducted on five benchmark datasets. Experimental results show that the proposed method yields comparable or better results in terms of PR curve, ROC curve, and F-measure, and is robust to deal with both cluttered and clean scenes.
