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Grayscale Image Segmentation Using Multilevel Thresholding and Nature-Inspired Algorithms

  • Genyun SunEmail author
  • Aizhu Zhang
  • Zhenjie Wang
Chapter

Abstract

Multilevel image thresholding plays a crucial role in analyzing and interpreting the digital images. Previous studies revealed that classical exhaustive search techniques are time consuming as the number of thresholds increased. To solve the problem, many nature-inspired algorithms (NAs) which can produce high-quality solutions in reasonable time have been utilized for multilevel thresholding. This chapter discusses three typical kinds of NAs and their hybridizations in solving multilevel image thresholding. Accordingly, a novel hybrid algorithm of gravitational search algorithm (GSA) with genetic algorithm (GA), named GSA-GA, is proposed to explore optimal threshold values efficiently. The chosen objective functions in this chapter are Kapur’s entropy and Otsu criteria. This chapter conducted experiments on two well-known test images and two real satellite images using various numbers of thresholds to evaluate the performance of different NAs.

Keywords

Image segmentation Multilevel thresholding Nature-inspired algorithms (NAs) Gravitational search algorithm (GSA) Genetic algorithm (GA) Kapur’s entropy Otsu 

Notes

Acknowledgments

This work was supported by the Chinese Natural Science Foundation Projects (No. 41471353, No. 41271349), Fundamental Research Funds for the Central Universities (No. 14CX02039A, No. 15CX06001A), and the China Scholarship Council.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of GeosciencesChina University of PetroleumQingdaoChina

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