Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Multi-objective Whale Optimization Algorithm for Multilevel Thresholding Segmentation

verfasst von : Mohamed Abd El Aziz, Ahmed A. Ewees, Aboul Ella Hassanien, Mohammed Mudhsh, Shengwu Xiong

Erschienen in: Advances in Soft Computing and Machine Learning in Image Processing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This chapter proposes a new method for determining the multilevel thresholding values for image segmentation. The proposed method considers the multilevel threshold as multi-objective function problem and used the whale optimization algorithm (WOA) to solve this problem. The fitness functions which used are the maximum between class variance criterion (Otsu) and the Kapur’s Entropy. The proposed method uses the whale algorithm to optimize threshold, and then uses this thresholding value to split the image. The experimental results showed the better performance of the proposed method to solving the multilevel thresholding problem for image segmentation and provided faster convergence with a relatively lower processing time.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation
Nächstes Kapitel Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images
Literatur
1.
Sarkar, S., Sen, N., Kundu, A., Das, S., Chaudhuri, S.S.: A differential evolutionary multilevel segmentation of near infra-red images using Renyis entropy. In: Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), Chicago, pp. 699-706. Springer, Heidelberg (2013)
2.
Zhao, F., Xie, X.: An overview of interactive medical image segmentation. Annals of the BMVA 7, 1–22 (2013)
3.
Pare, S., Bhandari, A.K., Kumar, A., Singh, G.K., Khare, S.: Satellite image segmentation based on different objective functions using genetic algorithm: a comparative study. In: 2015 IEEE International Conference on Digital Signal Processing (DSP), pp. 730-734. IEEE (2015)
4.
Kim, S.H., An, K.J., Jang, S.W., Kim, G.Y.: Texture feature-based text region segmentation in social multimedia data. Multimedia Tools Appl., 1–15 (2016)
5.
Ju, Z., Zhou, J., Wang, X., Shu, Q.: Image segmentation based on adaptive threshold edge detection and mean shift. In: 2013 4th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 385–388. IEEE (2013)
6.
Li, Z., Liu, C.: Gray level difference-based transition region extraction and thresholding. Comput. Electr. Eng. 35(5), 696–704 (2009)MathSciNetCrossRefMATH
7.
Tan, K.S., Isa, N.A.M.: Color image segmentation using histogram thresholding fuzzy c-means hybrid approach. Pattern Recogn. 44(1), 1–15 (2011)CrossRefMATH
8.
Zhou, C., Tian, L., Zhao, H., Zhao, K.: A method of two-dimensional Otsu image threshold segmentation based on improved firefly algorithm. In: Proceeding of IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems 2015, Shenyang, pp. 1420–1424 (2015)
9.
Guo, C., Li, H.: Multilevel thresholding method for image segmentation based on an adaptive particle swarm optimization algorithm. In: AI 2007: Advances in Artificial Intelligence, pp. 654–658. Springer, Heidelberg (2007)
10.
Zhang, Y., Lenan, W.: Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach. Entropy 13(4), 841–859 (2011)CrossRefMATH
11.
Bhandari, A.K., Singh, V.K., Kumar, A., Singh, G.K.: Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapurs entropy. Expert Syst. Appl. 41(7), 3538–3560 (2014)CrossRef
12.
Dirami, A., Hammouche, K., Diaf, M., Siarry, P.: Fast multilevel thresholding for image segmentation through a multiphase level set method. Signal Process. 93(1), 139–153 (2013)CrossRef
13.
Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13(6), 3066–3091 (2013)CrossRef
14.
Marciniak, A., Kowal, M., Filipczuk, P., Korbicz, J.: Swarm intelligence algorithms for multi-level image thresholding. In: Intelligent Systems in Technical and Medical Diagnostics, pp. 301–311. Springer, Heidelberg (2014)
15.
Jie, Y., Yang, Y., Weiyu, Y., Jiuchao, F.: Multi-threshold image segmentation based on K-means and firefly algorithm, pp. 134–142. Atlantis Press (2013)
16.
Yu, C., Jin, B., Lu, Y., Chen, X., et al.: Multi-threshold image segmentation based on firefly algorithm. In: Proceedings of Ninth International Conference on IIH-MSP 2013, Beijing, pp. 415–419 (2013)
17.
Vishwakarma, B., Yerpude, A.: A meta-heuristic approach for image segmentation using firefly algorithm. Int. J. Comput. Trends Technol. (IJCTT) 11(2), 69–73 (2014)CrossRef
18.
Sarkar, S., Ranjan, G.P., Das, S.: A differential evolution based approach for multilevel image segmentation using minimum cross entropy thresholding. In: International Conference on Swarm, Evolutionary, and Memetic Computing, pp. 51–58. Springer, Heidelberg (2011)
19.
Fayad, H., Hatt, M., Visvikis, D.: PET functional volume delineation using an ant colony segmentation approach. J. Nucl. Med. 56(supplement 3), 1745–1745 (2015)
20.
El Aziz, M.A., Ewees, A.A., Hassanien, A.E.: Hybrid swarms optimization based image segmentation. In: Hybrid Soft Computing for Image Segmentation, pp. 1–21. Springer International Publishing (2016)
21.
Djerou, L., Khelil, N., Dehimi, H.E., Batouche, M.: Automatic multilevel thresholding using binary particle swarm optimization for image segmentation. In: International Conference of Soft Computing and Pattern Recognition, 2009. SOCPAR’09, pp. 66–71. IEEE (2009)
22.
Ghamisi, P., Couceiro, M.S., Benediktsson, J.A., Ferreira, N.M.: An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. 39(16), 12407–12417 (2012)CrossRef
23.
Nakib, A., Roman, S., Oulhadj, H., Siarry, P.: Fast brain MRI segmentation based on two-dimensional survival exponential entropy and particle swarm optimization. In: 29th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society, 2007. EMBS 2007, pp. 5563–5566 (2007)
24.
Wei, C., Kangling, F.: Multilevel thresholding algorithm based on particle swarm optimization for image segmentation. In: 27th Chinese Conference in Control, 2008. CCC 2008, pp. 348–351. IEEE (2008)
25.
Yin, P.Y.: Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184(2), 503–513 (2007)MathSciNetMATH
26.
Zhiwei, Y., Zhengbing, H., Huamin, W., Hongwei, C.: Automatic threshold selection based on artificial bee colony algorithm. In: The 3rd International Workshop on Intelligent Systems and Applications (ISA), 2011, pp. 1–4 (2011)
27.
Horng, M.-H.: Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization. Expert Syst. Appl. 37(6), 4580–4592 (2010)CrossRef
28.
Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D., Perez-Cisneros, M.: Multilevel thresholding segmentation based on harmony search optimization. J. Appl. Math. 2013 (2013)
29.
Agrawal, S., Panda, R., Bhuyan, S., Panigrahi, B.K.: Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evolut. Comput. 11, 16–30 (2013)CrossRef
30.
Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13(6), 3066–3091 (2013)CrossRef
31.
Bhandari, A.K., Kumar, A., Singh, G.K.: Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapurs, Otsu and Tsallis functions. Expert Syst. Appl. 42(3), 1573–1601 (2015)
32.
Kapur, J.N., Sahoo P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graphics Image Process. 29(3), 273–285 (1985)
33.
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRef
34.
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001. Proceedings, vol. 2, pp. 416–423. IEEE (2001)
35.
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2004, vol. 2. IEEE (2003)
Metadaten
Titel
Multi-objective Whale Optimization Algorithm for Multilevel Thresholding Segmentation
verfasst von
Mohamed Abd El Aziz
Ahmed A. Ewees
Aboul Ella Hassanien
Mohammed Mudhsh
Shengwu Xiong
Copyright-Jahr
2018
Buch
Advances in Soft Computing and Machine Learning in Image Processing

Print ISBN: 978-3-319-63753-2

Electronic ISBN: 978-3-319-63754-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-63754-9_2

Premium Partner

    Bildnachweise