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Saliency Level Set Evolution

  • Conference paper
Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8835))

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Abstract

In this paper, we consider saliency detection problems from a unique perspective. We provide an implicit representation for the saliency map using level set evolution (LSE), and then combine LSE approach with energy functional minimization (EFM). Instead of introducing sophisticated segmentation procedures, we propose a flexible and lightweight LSE-EFM framework for saliency detection. The experimental results demonstrate our method outperforms several existing popular approaches. We then evaluate several computation strategies independently. The comparisons results indicate their effectiveness and strong abilities in combatting saliency confusions.

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Author information

Authors and Affiliations

  1. Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road., Shanghai, 200240, China

    Jincheng Mei & Bao-Liang Lu

  2. Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road., Shanghai, 200240, China

    Bao-Liang Lu

Authors
  1. Jincheng Mei
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  2. Bao-Liang Lu
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St., 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Mei, J., Lu, BL. (2014). Saliency Level Set Evolution. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-12640-1_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12639-5

  • Online ISBN: 978-3-319-12640-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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