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Superpixel Segmentation via Contour Optimized Non-Iterative Clustering

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Neural Computing for Advanced Applications (NCAA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

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Abstract

Superpixels intuitively over-segment an image into small partitions with homogeneity. Owing to the superiority of region-level description, it has been widely used in various computer vision applications as a substitute tool for pixels. However, there is still a disharmony between color homogeneity and shape regularity among existing superpixel algorithms, which hinders the performance of the task at hand. This paper introduces a novel Contour Optimized Non-Iterative Clustering (CONIC) superpixel segmentation method. It incorporates contour prior into the non-iterative clustering framework, thus providing a balanced trade-off between segmentation accuracy and visual uniformity. During the joint online assignment and updating step in the conventional Simple Non-Iterative Clustering (SNIC), a subtle feature distance is well-designed to measure the color similarity that considers contour constraint and prevents the boundary pixels from being assigned prematurely. Consequently, superpixels could acquire better visual quality and their boundaries are more consistent with the outlines of objects. Experiments on the Berkeley Segmentation Data Set 500 (BSDS500) verify that CONIC outperforms several state-of-the-art superpixel segmentation algorithms, in terms of both time efficiency and segmentation effects.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No.51805398 and 61972398), Project of Youth Talent Lift Program of Shaanxi University Association for science and technology (No.20200408).

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Authors and Affiliations

  1. Xidian University, Xi’an, China

    Jianglei Gong, Nannan Liao, Cheng Li, Wangpeng He & Baolong Guo

  2. China Academy of Space Technology, Beijing, China

    Jianglei Gong

  3. Qinghai GLI Technology Limited, Xining, China

    Xiaojun Ma

Authors
  1. Jianglei Gong
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  2. Nannan Liao
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  3. Cheng Li
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  4. Xiaojun Ma
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  5. Wangpeng He
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  6. Baolong Guo
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Corresponding author

Correspondence to Wangpeng He .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

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Gong, J., Liao, N., Li, C., Ma, X., He, W., Guo, B. (2021). Superpixel Segmentation via Contour Optimized Non-Iterative Clustering. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_46

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  • DOI: https://doi.org/10.1007/978-981-16-5188-5_46

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  • Online ISBN: 978-981-16-5188-5

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