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Improved Saliency Optimization Based on Superpixel-Wised Objectness and Boundary Connectivity

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

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Abstract

Salient object detection is one of the most challenging problems in computer vision and has extensive applications in many fields. Most existing algorithms detect salient object by employing various features. In this work, a bottom-up salient region measurement that integrates superpixel-wised objectness and boundary connectivity is proposed. Furthermore, to improve the result of the salient region measurement, an improved saliency optimization is put forward. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method, which can further improve the accuracy of saliency detection than other six state-of-the-art approaches on MSRA10k, DUT-OMRON and SED1.

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Authors and Affiliations

  1. School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi’an, 710072, China

    Yanzhao Wang & Guohua Peng

Authors
  1. Yanzhao Wang
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  2. Guohua Peng
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Corresponding authors

Correspondence to Yanzhao Wang or Guohua Peng .

Editor information

Editors and Affiliations

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

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Wang, Y., Peng, G. (2016). Improved Saliency Optimization Based on Superpixel-Wised Objectness and Boundary Connectivity. 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_19

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3004-8

  • Online ISBN: 978-981-10-3005-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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