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The Visual Computer

, Volume 35, Issue 6–8, pp 985–996 | Cite as

Weighted superpixel segmentation

  • Xin Qian
  • Xuemei LiEmail author
  • Caiming Zhang
Original Article
  • 189 Downloads

Abstract

Image boundaries and regularity are two important factors in superpixel segmentation. Balancing the influence of image boundaries and regularity is key to producing superpixels. In this paper, we present a novel superpixel segmentation algorithm, called weighted superpixel segmentation (WSS), which is capable of generating superpixels with high boundary adherence and regular shape in a linear time. In WSS, superpixels are generated according to a distance metric defined by the combination of a weight function term, color distance term and plane distance term. Unlike other superpixel algorithms, the weight function is calculated for each pixel to determine the weight of the color distance term and plane distance term in the distance metric. To increase superpixel regularity, superpixel seeds are initialized in a hexagonal manner. Then, we use the distance metric to obtain the initial superpixels. Determining the seed search range is an essential factor to improve algorithm accuracy; thus, a dynamic circle search range is designed in our algorithm that can provide better superpixel results. Finally, a merging strategy is applied to obtain the final superpixels and ensure that the number of superpixels agrees with expectations. Experimental results demonstrate that WSS performs as well as or even better than the existing methods in terms of several commonly used evaluation metrics in superpixel segmentation.

Keywords

Superpixel Boundary Weight Search range Merge 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.Shandong Co-Innovation Center of Future Intelligent ComputingYantaiChina
  3. 3.Shandong Province Key Lab of Digital Media TechnologyShandong University of Finance and EconomicsJinanChina

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