Abstract
Visual saliency aims to locate the noticeable regions or objects in an image. In this paper, a coarse-to-fine measure is developed to model visual saliency. In the proposed approach, we firstly use the contrast and center bias to generate an initial prior map. Then, we weight the initial prior map with boundary contrast to obtain the coarse saliency map. Finally, a novel optimization framework that combines the coarse saliency map, the boundary contrast and the smoothness prior is introduced with the intention of refining the map. Experiments on three public datasets demonstrate the effectiveness of the proposed method.
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Acknowledgments
This work was supported by the Key Science and Technology Planning Project of Hunan province, China (Grant No. 2014GK2007) and the Natural Science Foundation of Hunan Province, China (Grant No. 2015JJ4014).
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Zhang, H., Xu, M., Zhuo, L. et al. A novel optimization framework for salient object detection. Vis Comput 32, 31–41 (2016). https://doi.org/10.1007/s00371-014-1053-z
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DOI: https://doi.org/10.1007/s00371-014-1053-z