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Weighted Linear Multiple Kernel Learning for Saliency Detection

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2nd EAI International Conference on Robotic Sensor Networks

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent.

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Notes

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Acknowledgements

This work was partly supported by the National Science Foundation (Grant No. IIS-1302164), the National Natural Science Foundation of China (Grant No. 61881240048, 61571240, 61501247, 61501259, 61671253), China Postdoctoral Science Foundation (Grant No. 2015M581841), Open Fund Project of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education (Nanjing University of Science and Technology) (Grant No. JYB201709, JYB201710), and Natural Science Foundation of Jiangsu Province, China (BK20160908), NUPTSF (Grant No. NY214139).

Author information

Authors and Affiliations

  1. National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China

    Quan Zhou, Yawen Fan, Xiaofu Wu & Baoyu Zheng

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, P. R. China

    Quan Zhou

  3. College of Information Engineering, China University of Geosciences, Wuhan, P. R. China

    Jinwen Wu

  4. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China

    Suofei Zhang

  5. Department of Cyber Security, Beijing Electronic Science and Technology Institute, Beijing, China

    Xin Jin

  6. CETC Big Data Research Institute Co., Ltd., Guiyang, Guizhou, China

    Xin Jin

  7. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

  8. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA

    Longin Jan Latecki

Authors
  1. Quan Zhou
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  7. Xin Jin
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  8. Huimin Lu
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Corresponding author

Correspondence to Quan Zhou .

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Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

  2. Department of Electronics Engineering and Computer Science, Fukuoka University, Fukuoka, Japan

    Li Yujie

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Zhou, Q. et al. (2020). Weighted Linear Multiple Kernel Learning for Saliency Detection. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_19

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  • DOI: https://doi.org/10.1007/978-3-030-17763-8_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17762-1

  • Online ISBN: 978-3-030-17763-8

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