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Learning Optimal Seeds for Salient Object Detection

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Advances in Brain Inspired Cognitive Systems (BICS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10023))

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Abstract

Visual saliency detection is useful for applications as object recognition, resizing and image segmentation. It is a challenge to detect the most important scene from the input image. In this paper, we present a new method to get saliency map. First, we evaluate the salience value of each region by global contrast based spatial and color feature. Second, the salience values of the first stage are used to optimize the background and foreground queries (seeds), and then manifold ranking is employed to compute two phase saliency maps. Finally, the final saliency map is got by combining the two saliency map. Experiment results on four datasets indicate the significantly improved accuracy of the proposed algorithm in comparison with eight state-of-the-art approaches.

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Author information

Authors and Affiliations

  1. School of Computer and Information, Fuyang Normal College, Fuyang, Anhui, China

    Huiling Wang

  2. School of Computer Science and Technology, Anhui University, Hefei, Anhui, China

    Huiling Wang, Lixiang Xu & Bin Luo

Authors
  1. Huiling Wang
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  2. Lixiang Xu
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  3. Bin Luo
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Corresponding author

Correspondence to Bin Luo .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  2. Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom

    Amir Hussain

  3. Anhui University, Anhui, China

    Bin Luo

  4. National University of Singapore, Singapore, Singapore

    Kay Chen Tan

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  6. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

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© 2016 Springer International Publishing AG

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Wang, H., Xu, L., Luo, B. (2016). Learning Optimal Seeds for Salient Object Detection. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-49685-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49684-9

  • Online ISBN: 978-3-319-49685-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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