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Quantum-Inspired Bat Optimization Algorithm for Automatic Clustering of Grayscale Images

  • Alokananda DeyEmail author
  • Siddhartha Bhattacharyya
  • Sandip Dey
  • Jan Platos
  • Vaclav Snasel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 922)

Abstract

This paper introduces a quantum-inspired framework with the bat optimization algorithm for automatic clustering of image datasets. The aim of this work is to find out the optimal number of clusters from an image dataset on a run. A comparison has been produced between the quantum-inspired bat optimization algorithm and its classical counterpart. As a result, it is seen that the quantum-inspired version outperforms its classical counterpart. Computational experiments have been conducted on four Berkeley image datasets.

Keywords

Automatic clustering Metaheuristic algorithm Quantum computing Bat optimization algorithm Statistical test \( (t - \text{test}) \) 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Alokananda Dey
    • 1
    Email author
  • Siddhartha Bhattacharyya
    • 1
    • 2
  • Sandip Dey
    • 3
  • Jan Platos
    • 2
  • Vaclav Snasel
    • 2
  1. 1.Department of Computer ApplicationRCC Institute of Information TechnologyKolkataIndia
  2. 2.Faculty of Electrical Engineering and Computer ScienceVSB Technical University of OstravaOstravaCzech Republic
  3. 3.Department of Computer Science and EngineeringOmDayal Group of InstitutionsHowrahIndia

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