Abstract
As an important problem in computer vision, saliency detection is essential for image segmentation, super-resolution, object recognition, etc. In this paper, we propose a novel method for saliency detection on image using region contrast and machine learning approaches. An image boundary extension-based general framework is proposed that can be used for all rarity- or sparsity-based schemes to improve their performances. Then, a saliency map based on boundary extension and region contrast is constructed. Due to its unsatisfactory performance, another saliency map combining supervised locality-preserving projection and support vector regression is built, to complement the previous saliency map. A final saliency map can be obtained by fusing these two saliency maps. The proposed method is evaluated on the publicly available dataset MSRA-1000 and compared with 13 state-of-the-art methods. Experimental results indicate that the proposed method outperforms existing schemes both in qualitative and quantitative comparisons.
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Acknowledgments
This work was supported by the Fund of Jilin Provincial Science and Technology Department (No. 20130206042GX, No. 20140204089GX), Young Scientific Research Fund Of Jilin Province Science And Technology Development Project (No. 201201070, No. 201201063, No. 20130522115JH) and National Natural Science Foundation of China (No. 11271064).
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Shi, Y., Yi, Y., Yan, H. et al. Region contrast and supervised locality-preserving projection-based saliency detection. Vis Comput 31, 1191–1205 (2015). https://doi.org/10.1007/s00371-014-1005-7
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DOI: https://doi.org/10.1007/s00371-014-1005-7