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Salient Object Detection via Graph-Based Flexible Manifold Ranking

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Advances in Brain Inspired Cognitive Systems (BICS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11691))

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Abstract

The task of saliency detection is to segment salient objects in natural scenes. Simple and effective saliency detection model has always been a challenging problem. We explore a graph-based flexible manifold ranking approach for single image saliency detection. An input image is represented as an undirected graph. Feature vectors are extracted covering regional color and texture. An optimal function is used to infer the labels based on linear classification projection and manifold ranking in our work. The optimal function further ensures the reliability of the prediction results. Extensive experiments on four benchmark datasets show that our method is better than the other eight classic methods. So the proposed method is a competitive method.

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Acknowledgement

This work is supported by Natural Science Foundation of Anhui Province (1908085QF264, 1808085QF209) and the Natural Science Foundation of Anhui Higher Education Institution of China (KJ2019A0536, KJ2019A0532, KJ2019A0529, KJ2019A0026 and KJ2019A0541).

Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Fuyang Normal University, Fuyang, 236037, China

    Ying Yang

  2. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Bo Jiang, Yun Xiao & Jin Tang

Authors
  1. Ying Yang
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  2. Bo Jiang
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  3. Yun Xiao
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  4. Jin Tang
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Corresponding author

Correspondence to Jin Tang .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Jinchang Ren

  2. Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  3. Guangdong Polytechnic Normal University, Guangzhou, China

    Huimin Zhao

  4. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Kaizhu Huang

  5. Northwestern Polytechnical University, Xi'an, China

    Jiangbin Zheng

  6. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  7. Guangdong Polytechnic Normal University, Guangzhou, China

    Rongjun Chen

  8. Guangdong Polytechnic Normal University, Guangzhou, China

    Yinyin Xiao

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Yang, Y., Jiang, B., Xiao, Y., Tang, J. (2020). Salient Object Detection via Graph-Based Flexible Manifold Ranking. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_38

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  • DOI: https://doi.org/10.1007/978-3-030-39431-8_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39430-1

  • Online ISBN: 978-3-030-39431-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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