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Improved bat algorithm applied to multilevel image thresholding.

Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligenc...

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Published in:Scientific World Journal pp. 176718 - 176719
Main Authors: Alihodzic, Adis, Tuba, Milan
Format: research Journal Article
Published: Wiley-Blackwell 2014
Online Access:View this record in EBSCOhost
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      dt: 2014
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2014/176718
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        atl: Improved bat algorithm applied to multilevel image thresholding.
      aug:
        au:
          Alihodzic, Adis
          Tuba, Milan
      sug:
        subj:
          Artificial Intelligence
          Models, Theoretical
          Image Processing, Computer Assisted Methods
          Algorithms
          Comparative Studies
          Multicenter Studies
          Evaluation Research
          Validation Studies
          Ferrans and Powers Quality of Life Index
      ab: Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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