Abstract
The goal of salient object detection is to estimate the regions which are most likely to attract human’s visual attention. As an important image preprocessing procedure to reduce the computational complexity, salient object detection is still a challenging problem in computer vision. In this paper, we proposed a salient object detection model by integrating local and global superpixel contrast at multiple scales. Three features are computed to estimate the saliency of superpixel. Two optimization measures are utilized to refine the resulting saliency map. Extensive experiments with the state-of-the-art saliency models on four public datasets demonstrate the effectiveness of the proposed model.
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Foundation item: the Natural Science Foundation of China (Nos. 61602349, 61375053, and 61273225), the China Scholarship Council (No. 201508420248), and Hubei Chengguang Talented Youth Development Foundation (No. 2015B22)
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Mu, N., Xu, X., Wang, Y. et al. A multiscale superpixel-level salient object detection model using local-global contrast cue. J. Shanghai Jiaotong Univ. (Sci.) 22, 121–128 (2017). https://doi.org/10.1007/s12204-017-1810-z
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DOI: https://doi.org/10.1007/s12204-017-1810-z