Abstract
In order to obtain more refined salient object detection results, firstly, the coarse salient regions are extracted from the bottom-up, the coarse saliency map contains local map, frequency prior map and global color distribution map, which are more in accord with the rules of biological psychology. Then, an algorithm is proposed to measure the background prior quality by using three indexes, namely, salient expectation, local contrast and global contrast. Finally, the weighted algorithm is designed according to the prior quality to improve the saliency, so that the saliency prior and the saliency detection results are more accurate. Compared with 9 state-of-the-art algorithms on the 2 benchmark datasets of ECSSD and MSRA 10k, the proposed algorithm highlights salient regions, reduces noise, and is more in line with human visual perception, and reflects the excellence.
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Acknowledgements
This work was supported by “National Natural Science Foundation of China (No. 61300170)” and “Anhui province higher education to enhance the general project plan of Provincial Natural Science Research (No. TSKJ2014B11)”. The authors wish to thank the Education Department of Anhui Province for their help.
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Gu, L., Pan, Z. (2018). Salient Object Detection Based on the Fusion of Foreground Coarse Extraction and Background Prior. In: Pan, Z., Cheok, A., Müller, W. (eds) Transactions on Edutainment XIV. Lecture Notes in Computer Science(), vol 10790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56689-3_9
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