Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Hybrid Swarms Optimization Based Image Segmentation Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Novel Hybrid CS-BFO Algorithm for Optimal Multilevel Image Thresholding Using Edge Magnitude Information

Authors : Sanjay Agrawal, Leena Samantaray, Rutuparna Panda

Published in: Hybrid Soft Computing for Image Segmentation

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Thresholding is the key to simplify image classification. It becomes challenging when the number of thresholds is more than two. Most of the existing multilevel thresholding techniques use image histogram information (first-order statistics). This chapter utilizes optimal edge magnitude information (second-order statistics) of an image to obtain multilevel threshold values. We compute the edge magnitude information from the gray-level co-occurrence matrix (GLCM) of the image. The second-order statistics uses the correlation among the pixels for improved results. Maximization of edge magnitude is vital for obtaining optimal threshold values. The edge magnitude is maximized by introducing a novel hybrid cuckoo search-bacterial foraging optimization (CS-BFO) algorithm. The novelty of our proposed CS-BFO algorithm lies in its ability to provide improved chemotaxis in BFO algorithm, which is achieved by supplementing levy flight feature of CS. Social foraging models are relatively efficient for determining optimum multilevel threshold values. Hence, CS-BFO is used for improved thresholding performance and highlighting the novelty of this contribution. We have also implemented other soft computing tools cuckoo search (CS), particle swarm optimization (PSO), and genetic algorithm (GA) for a comparison. In addition, we have incorporated constraint handling in all the above-mentioned techniques so that optimal threshold values do not cross the bounds. This study reveals the fact that CS technique provides us improved speed while the CS-BFO method shows improved results both qualitatively and quantitatively.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter Grayscale Image Segmentation Using Multilevel Thresholding and Nature-Inspired Algorithms
next chapter REFII Model and Fuzzy Logic as a Tool for Image Classification Based on Image Example
Literature
1.
2.
Haralick, R.M.: Image segmentation survey. In: Faugeras, O.D. (ed.), Fundamentals in Computer Vision, pp. 209–224. Cambridge University Press, Cambridge (1983)
3.
Haralick, R.M., Shapiro, L.G.: Image segmentation techniques. Comput. Vis. Gr. Image Process. 29(1), 100–132 (1985)CrossRef
4.
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognit. 26(9), 1277–1294 (1993)CrossRef
5.
Kapur, J.N., Sahoo, P.K., Wong, A.K.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Gr. Image Process. 29(3), 273–285 (1985)CrossRef
6.
Otsu, N.: A threshold selection method from gray level histograms. Automatica 11(285–296), 23–27 (1975)
7.
Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognit. 19(1), 41–47 (1986)CrossRef
8.
Sahoo, P.K., Soltani, S., Wong, A.K.: A survey of thresholding techniques. Comput. Vis. Gr. Image Process. 41(2), 233–260 (1988)CrossRef
9.
Zhang, Y.J.: An Overview of Image and Video Segmentation in the Last 40 years. In: Advances in Image and Video Segmentation, pp. 1–15 (2006)
10.
Lee, S.U., Yoon Chung, S., Park, R.H.: A comparative performance study of several global thresholding techniques for segmentation. Comput. Vis. Gr. Image Process. 52(2), 171–190 (1990)CrossRef
11.
Sankur, B., Sezgin, M.: Image thresholding techniques: a survey over categories. Pattern Recognit. 34(2), 1573–1583 (2001)
12.
Chang, C.I., Du, Y., Wang, J., Guo, S.M., Thouin, P.D.: Survey and comparative analysis of entropy and relative entropy thresholding techniques. IEE Proc. Vis. Image Signal Process. 153(6), 837–850 (2006)CrossRef
13.
Yin, P.Y.: A fast scheme for optimal thresholding using genetic algorithms. Signal Process. 72(2), 85–95 (1999)CrossRefMATH
14.
Cheng, H., Chen, Y.H., Sun, Y.: A novel fuzzy entropy approach to image enhancement and thresholding. Signal Process. 75(3), 277–301 (1999)CrossRefMATH
15.
Zahara, E., Fan, S.K., Tsai, M.D.: Optimal multi-thresholding using a hybrid optimization approach. Pattern Recognit. Lett. 26(8), 1082–1095 (2005)CrossRef
16.
Sathya, P.D., Kayalvizhi, R.: Optimal multilevel thresholding using Bacterial foraging algorithm. Expert Syst. Appl. 38(12), 15549–15564 (2011)CrossRef
17.
Sarkar, S., Sen, N., Kundu, A., Das, S., Chaudhury, S.: A differential evolutionary multilevel segmentation of near infra-red images using Renyi entropy. In: International Conference on Frontiers of Intelligent Computing: Theory and Applications, pp. 699–706. Springer, Heidelberg (2013)
18.
Sarkar, S., Patra, G.R., Das, S.: A differential evolution based approach for multilevel image segmentation using minimum cross entropy thresholding. In: Swarm, Evolutionary and Memetic Computing, pp. 51–58. Springer, Heidelberg (2011)
19.
Zhang, Y., Wu, L.: Optimal multi-level thresholding based on maximum tsallis entropy via an artificial bee colony approach. Entropy 13(4), 841–859 (2011)CrossRefMATH
20.
Horng, M.H.: A multilevel image thresholding using the honey bee mating optimization. Appl. Math. Comput. 215(9), 3302–3310 (2010)MathSciNetMATH
21.
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 Kapur’s entropy. Expert Syst. Appl. 41(7), 3538–3560 (2014)CrossRef
22.
Raja, N.S., Sukanya, S.A., Nikita, Y.: Improved PSO based multi-level thresholding for cancer infected breast thermal images using otsu. Proc. Comput. Sci. 48, 524–529 (2015)CrossRef
23.
Bhandari, A.K., Kumar, A., Singh, G.K.: Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst. Appl. 42(3), 1573–1601 (2015)CrossRef
24.
Nabizadeh, N., John, N., Wright, C.: Histogram-based gravitational optimization algorithm on single MR modality for automatic brain lesion detection and segmentation. Expert Syst. Appl. 41(17), 7820–7836 (2014)CrossRef
25.
Liu, Y., Mu, C., Kou, W., Liu, J.: Modified particle swarm optimization-based multilevel thresholding for image segmentation. Soft Comput. 19(5), 1311–1327 (2014)CrossRef
26.
Brajevic, I., Tuba, M.: Cuckoo search and firefly algorithm applied to multilevel image thresholding. In: Cuckoo Search and Firefly Algorithm, pp. 115–139. Springer International Publishing, Heidelberg (2014)
27.
Roy, S., Kumar, U., Chakraborty, D., Nag, S., Mallick, A., Dutta, S.: Comparative analysis of cuckoo search optimization-based multilevel image thresholding. In Intelligent Computing, Communication and Devices, pp. 327–342. Springer, India (2015)
28.
Baraldi, A., Parmiggiani, F.: An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters. IEEE Trans. Geosci. Remote Sens. 33(2), 293–304 (1995)CrossRef
29.
Chanda, B., Majumder, D.D.: A note on the use of the gray-level co-occurrence matrix in threshold selection. Signal Process. 15, 149–167 (1988)CrossRef
30.
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Publishing, New Jersey (1992)
31.
Albregtsen, F.: Statistical Texture Measures Computed from Gray Level Co-occurrence Matrices, Image Processing Laboratory. Department of Informatics, University of Oslo (1995)
32.
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)CrossRef
33.
Mokji, M.M., Abu Bakar, S.A.: Adaptive thresholding based on co-occurrence matrix edge information. In: First Asia International Conference on Modelling and Simulation, pp. 444–450. IEEE, New York (2007)
34.
Yang, X.S., Deb, S.: Cuckoo search via levy flights. In: World Congress on Nature and Biologically Inspired Computing, pp. 210–214. IEEE, New York (2009)
35.
Yang, X.S., Deb, S.: Engineering optimization by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)MATH
36.
Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)CrossRef
37.
Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)
38.
Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization and Control. IEEE Control Syst. 22(3), 52–67 (2002)MathSciNetCrossRef
39.
40.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRef
41.
Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)MathSciNetCrossRef
42.
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
Metadata
Title
A Novel Hybrid CS-BFO Algorithm for Optimal Multilevel Image Thresholding Using Edge Magnitude Information
Authors
Sanjay Agrawal
Leena Samantaray
Rutuparna Panda
Copyright Year
2016
Book
Hybrid Soft Computing for Image Segmentation

Print ISBN: 978-3-319-47222-5

Electronic ISBN: 978-3-319-47223-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-47223-2_3

Premium Partner

    Image Credits