Abstract
Saliency region detection plays an important role in computer vision aiming at discovering the salient objects in an image. This paper proposes a novel saliency detection algorithm (named as GMSL) via combining graph model and statistical learning. Firstly, the algorithm generates an initial saliency map by manifold ranking and optimizes it with absorbing Markov chain, both of which are based on graph model. Then, Bayes estimation with color statistical models is utilized as statistical learning to assign the saliency values to each pixel and further purify the map. Extensive experiments comparing with several state-of-the-art saliency detection works tested on different datasets demonstrate the superiority of the proposed algorithm.
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Acknowledgments
This work was partially sponsored by Project 61375031, 61471048, and 61573068 supported by National Natural Science Foundation of China. This work was also supported by the Beijing Higher Education Young Elite Teacher Program, Beijing Nova Program, CCF-Tencent Open Research Fund, and the Program for New Century Excellent Talents in University.
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Huang, L., Tang, S., Hu, J., Deng, W. (2016). Saliency Region Detection via Graph Model and Statistical Learning. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_1
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DOI: https://doi.org/10.1007/978-981-10-3005-5_1
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