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A Bi-level Image Segmentation Framework Using Gradient Ascent

  • Cheng Li
  • Baolong GuoEmail author
  • Xinxing Guo
  • Yulin Yang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 834)

Abstract

In order to solve the problem of under-segmentation in traditional superpixel methods, a new image segmentation framework is proposed, which is based on gradient ascent including Simple Linear Iterative Clustering (SLIC) superpixels and watershed algorithm. First, SLIC method is adopted to generate uniform superpixels, which are then determined whether under-segmentation occurs by a homogeneity criterion. In heterogeneous regions, an adaptive watershed algorithm processes a more precise division based on luminance histogram. Experimental results show that the bi-level framework has good performance on detail-rich regions, without significantly increasing the time complexity compared with conventional SLIC.

Keywords

Segmentation Superpixel Watershed Subdivision 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61571346). The research is also supported by the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Cheng Li
    • 1
    • 2
  • Baolong Guo
    • 1
    • 2
    Email author
  • Xinxing Guo
    • 1
    • 2
  • Yulin Yang
    • 1
    • 2
  1. 1.Institute of Intelligent Control and Image EngineeringXidian UniversityShaanxiChina
  2. 2.School of Aerospace Science and TechnologyXidian UniversityShaanxiChina

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