165
Views
5
CrossRef citations to date
0
Altmetric
Research Articles

Image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering

, , , &
Pages 63-75 | Received 21 May 2018, Accepted 14 Nov 2018, Published online: 05 Dec 2018

References

  • Akhtar N, Agarwal N, Burjwal A. K-mean algorithm for image segmentation using neutrosophy. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI); New Delhi. IEEE; 2014. p. 2417–2421.
  • Kang J, Min L, Luan Q, et al. Novel modified fuzzy c-means algorithm with applications. Digit Signal Process. 2009;19:309–319. doi: 10.1016/j.dsp.2007.11.005
  • Linda O, Manic M. General type-2 fuzzy C-means algorithm for uncertain fuzzy clustering. IEEE Trans Fuzzy Syst. 2012;20(5):883–897. doi: 10.1109/TFUZZ.2012.2187453
  • Cheng H, Guo Y, Zhang Y. A novel image segmentation approach based on neutrosophic set and improved fuzzy c-means algorithm. N Math Nat Comput. 2011;07(01):155–171. doi: 10.1142/S1793005711001858
  • Hanbay K, Talu MF. Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set. Appl Soft Comput. 2014;21:433–443. doi: 10.1016/j.asoc.2014.04.008
  • Karabatak E, Guo Y, Sengur A. Modified neutrosophic approach to color image segmentation. J Electron Imaging. 2013;22(1):013005. doi: 10.1117/1.JEI.22.1.013005
  • Mathew JM, Simon P. Color texture image segmentation based on neutrosophic set and nonsubsampled contourlet transformation. In: Gupta P, Zaroliagis C, editors. Proceedings of the first International Conference on Applied Algorithms, ICAA 2014; Kolkata. Cham: Springer International Publishing; 2014. p. 164–173.
  • Yu B, Niu Z, Wang L. Mean shift based clustering of neutrosophic domain for unsupervised constructions detection. Opt Int J Light Electron Opt. 2013;124(21):4697–4706. doi: 10.1016/j.ijleo.2013.01.117
  • Awad MM. Toward robust segmentation results based on fusion methods for very high resolution optical image and LiDAR data. IEEE J Sel Topics Appl Earth Observ. 2017;10(5):2067–2076. doi: 10.1109/JSTARS.2017.2653061
  • Choy SK, Shu YL, Yu KW, et al. Fuzzy model-based clustering and its application in image segmentation. Pattern Recognit. 2017;68:141–157. doi: 10.1016/j.patcog.2017.03.009
  • Pal SK. Fuzzy tools for the management of uncertainty in pattern recognition, image analysis, vision and expert systems. Int J Syst Sci. 1991;22:511–549. doi: 10.1080/00207729108910632
  • Guo Y, Sengur A. A novel image segmentation algorithm based on neutrosophic filtering and level set. Neutrosophic Sets Syst. 2013;1:46–49.
  • Sengur A, Guo Y. Color texture image segmentation based on neutrosophic set and wavelet transformation. Comput Vis Image Underst. 2011;115(8):1134–1144. doi: 10.1016/j.cviu.2011.04.001
  • Ye J. Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. Int J Gen Syst. 2013;42(4):386–394. doi: 10.1080/03081079.2012.761609
  • Guo Y, Sengur A. NCM: neutrosophic c-means clustering algorithm. Pattern Recogn. 2015;48(8):2710–2724. doi: 10.1016/j.patcog.2015.02.018
  • Guo Y, Xia R, Şengür A, et al. A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering. Neural Comput Appl. 2017;28(10):3009–3019. doi: 10.1007/s00521-016-2441-2
  • Zhang L, Zhang M, Cheng H-D. Color image segmentation based on neutrosophy. Optical Eng. 2012;51(3):037009. doi: 10.1117/1.OE.51.3.037009
  • Guo Y, Sengur A. A novel image segmentation algorithm based on neutrosophic filtering and level set. Neutrosophic Sets Syst. 2013;2013(1):46–49.
  • Guo Y, Sengur A. A novel image segmentation algorithm based on neutrosophic similarity clustering. Appl Soft Comput. 2014;25:391–398. doi: 10.1016/j.asoc.2014.08.066
  • Wang S, Chung F-L, Xiong F. A novel image thresholding method based on Parzen window estimate. Pattern Recognit. 2008;41(1):117–129. doi: 10.1016/j.patcog.2007.03.029
  • Gong M, Liang Y, Shi J, et al. Fuzzy C-means clustering With local information and kernel metric for image segmentation. IEEE Trans Image Process. 2013;22(2):573–584. doi: 10.1109/TIP.2012.2219547
  • Bezdek JC. Mathematical models for systematics and taxonomy. In: G Estabrook, editor. Proceedings of eighth international conference on Numerical Taxonomy. San Franscisco (CA): Freeman; 1975. p. 143–166.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.