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Merging fixation prediction and manifold learning for salient object segmentation. (Chinese. English summary) Zbl 1374.68707

Summary: As the priors of existing saliency segmentation methods are not robust enough in the complex background, an algorithm which merged fixation prediction and manifold learning was proposed to effectively segment salient objects in complex scenes. The algorithm predicted and segmented salient objects in scenes by introducing the prior of fixation and extracting the map of superpixels. To further improve the performance of saliency segmentation, the algorithm leveraged color contrast between superpixels as features in CIE-Lab (color model) space and resolved the saliency optimization of coarse regions via a manifold learning-based method which improved the segmentation accuracy. Experimental results show that the proposed method has an improvement of 21.8% than the other best methods on complex datasets and is more robust to segment salient objects in different environments.

MSC:

68U10 Computing methodologies for image processing
68T05 Learning and adaptive systems in artificial intelligence
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