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Spherical Superpixels: Benchmark and Evaluation

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11366))

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Abstract

Although a variety of superpixel algorithms have been developed and adopted as elementary tools in low-level computer vision and multimedia applications, most of them are designed for planar images. The quick growth of spherical panoramic images raises the urgent need of spherical superpixel algorithms and also a unifying benchmark of spherical image segmentation for the quantitative evaluation. In this paper, we present a general framework to establish spherical superpixel algorithms by extending planar counterparts, under which two spherical superpixel algorithms are developed. Furthermore, we propose the first segmentation benchmark of real-captured spherical images, which are manually annotated via a three-stage process. We use this benchmark to evaluate eight algorithms, including four spherical ones and the four corresponding planar ones, and discuss the results with respect to quantitative segmentation quality, runtime as well as visual quality.

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Notes

  1. 1.

    The annotated dataset are available at http://scs.tju.edu.cn/~lwan/data/spsdataset/spsdataset75.rar.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (61572354, 61671325, 61702479).

Author information

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Liang Wan, Xiaorui Xu & Wei Feng

  2. Key Research Center for Surface Monitoring and Analysis of Cultural Relics (SMARC), SACH, Tianjin, China

    Liang Wan, Xiaorui Xu & Wei Feng

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

    Qiang Zhao

Authors
  1. Liang Wan
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  2. Xiaorui Xu
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  3. Qiang Zhao
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  4. Wei Feng
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Corresponding author

Correspondence to Qiang Zhao .

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Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Wan, L., Xu, X., Zhao, Q., Feng, W. (2019). Spherical Superpixels: Benchmark and Evaluation. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_44

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  • DOI: https://doi.org/10.1007/978-3-030-20876-9_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20875-2

  • Online ISBN: 978-3-030-20876-9

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