Abstract
Detection of salient objects from images is gaining increasing research interest in recent years. Consider that the edge may not always be clear for some traditional saliency detection methods, a novel framework for saliency detection based on Histogram-based Contrast (HC) method and guided filter is proposed in this paper. Firstly, the algorithm based on HC is used to get a preliminary saliency map. Secondly, through a series of operation on the original input image, the approximate location of the salient object can be obtained. Then, the location map of salient object is generated by implementing guided image filtering on the approximate location and will be transformed into a binary image in the following process. Finally, the final saliency map can be achieved by fusing the binary map and the HC saliency map. The obtained saliency map not only inherits the good ability of HC method to maintain the internal information of the salient objects, but also has the advantage of the location map to well extract the edge of the salient object.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant 61305040 and the Fundamental Research Funds for the Central Universities under Grant JB161305.
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Zeng, P., Meng, F., Shi, R., Shan, D., Wang, Y. (2017). Salient Object Detection Based on Histogram-Based Contrast and Guided Image Filtering. In: Pan, JS., Snášel, V., Sung, TW., Wang, X. (eds) Intelligent Data Analysis and Applications. ECC 2016. Advances in Intelligent Systems and Computing, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-319-48499-0_11
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DOI: https://doi.org/10.1007/978-3-319-48499-0_11
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