| Peer-Reviewed

Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation

Received: 17 September 2014     Accepted: 22 September 2014     Published: 20 October 2014
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Abstract

In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.

Published in Science Journal of Circuits, Systems and Signal Processing (Volume 3, Issue 6-1)

This article belongs to the Special Issue Computational Intelligence in Digital Image Processing

DOI 10.11648/j.cssp.s.2014030601.11
Page(s) 1-5
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Fuzzy C Means, Image Segmentation, Unsupervised Learning, AUFCM and DCTS

References
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[3] Chunming Li, Rui Huang, Zhaohua Ding, J. Chris Gatenby, Dimitris N. Metaxas, “A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI”, IEEE Transactions on Image processing, vol. 20, no. 7, july 2011.
[4] Yan-Ying Chen , “Automatic Training Image Acquisition and Effective Feature Selection From Community-Contributed Photos for Facial Attribute Detection”, Multimedia, IEEE Transactions on (Volume:15 , Issue: 6 ), Oct 2013
[5] Chen Zheng ; Sch. of Math. & Inf. Sci., Henan Univ., Kaifeng, China ; Leiguang Wang ; Rongyuan Chen ; Xiaohui Chen, ” Image Segmentation Using Multiregion-Resolution MRF Model”, Geoscience and Remote Sensing Letters, IEEE (Volume:10 , Issue: 4 ), July 2013
[6] Rong Xiang, Yibin Ying, Huanyu Jiang, Rong Xiang,” Research on Image Segmentation Methods of Tomato in Natural Conditions”, 2011 4th International Congress on Image and Signal Processing.
[7] Said, A.F. ; Sch. of Electr., Comput., & Energy Eng., Arizona State Univ., Tempe, AZ, USA ; Bennett, B.L. ; Karam, L.J. ; Pettinato, J.S.,” Automated Detection and Classification of Non-Wet Solder Joints”, Automation Science and Engineering, IEEE Transactions on (Volume:8 , Issue: 1), Jan 2011
[8] SaminaNaz, HammadMajeed, HumayunIrshad,” Image Segmentation using Fuzzy Clustering: A Survey”, IEEE Transaction 2010 6th International Conference on Emerging Technologies.
[9] KusumBharti, SanyamShukla, Shweta Jain,” Intrusion Detection using Unsupervised Learning”, International Journal on Computer Science and Engineering Vol. 02, No. 05, 2010.
[10] Satish R. Kolhe, Ranjana S. Zinjore,” Clustering Iris Data using Supervised and Unsupervised Learning”, International Journal of Computer Science and Application Issue 2010.
[11] Dr. Ch. G.V.N. Prasad, K. HanumanthaRao, DepaPratima and B.N. Alekhya, ”Unsupervised Learning Algorithms to Identify the Dense Cluster in Large Datasets”, International Journal of Computer Science and Telecommunications [Volume 2, Issue 4, July 2011].
[12] Jie Cui ; Inst. of Biornaterials& Biomed. Eng., Toronto Univ., Ont. ; Loewy, J. ; Kendall, E.J. “Automated search for arthritic patterns in infrared spectra of synovial fluid using adaptive wavelets and fuzzy C-Means analysis”, Biomedical Engineering, IEEE Transactions on (Volume:53 , Issue: 5 May 2006)
[13] FasahatUllahSiddiqui and NorAshidi Mat Isa, “Enhanced Moving K-Means (EMKM) Algorithm for Image Segmentation”, IEEE Transaction [Volume:57, Issue: 2,May 2011].
[14] Albert Huang, A. ; Dept. of Electr. &Comput. Eng., Univ. of British Columbia, Vancouver, BC ; Abugharbieh, R. ; Tam, R, A Hybrid Geometric–Statistical Deformable Model Biomedical Engineering,for Automated 3-D Segmentation in Brain MRI”, IEEE Transactions on (Volume: 56, Issue: 7 2009), July 2009
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  • APA Style

    Arunkumar Rajendran, Thamarai Muthusamy. (2014). Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation. Science Journal of Circuits, Systems and Signal Processing, 3(6-1), 1-5. https://doi.org/10.11648/j.cssp.s.2014030601.11

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    ACS Style

    Arunkumar Rajendran; Thamarai Muthusamy. Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation. Sci. J. Circuits Syst. Signal Process. 2014, 3(6-1), 1-5. doi: 10.11648/j.cssp.s.2014030601.11

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    AMA Style

    Arunkumar Rajendran, Thamarai Muthusamy. Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation. Sci J Circuits Syst Signal Process. 2014;3(6-1):1-5. doi: 10.11648/j.cssp.s.2014030601.11

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  • @article{10.11648/j.cssp.s.2014030601.11,
      author = {Arunkumar Rajendran and Thamarai Muthusamy},
      title = {Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation},
      journal = {Science Journal of Circuits, Systems and Signal Processing},
      volume = {3},
      number = {6-1},
      pages = {1-5},
      doi = {10.11648/j.cssp.s.2014030601.11},
      url = {https://doi.org/10.11648/j.cssp.s.2014030601.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.s.2014030601.11},
      abstract = {In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.},
     year = {2014}
    }
    

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    AB  - In this paper an optimized method for unsupervised image clustering is proposed. Generally a Novel Fuzzy C Means (FCM) or FCM based clustering algorithm are used for clustering based image segmentation but these algorithms have a disadvantage of depending upon supervised user inputs such as number of clusters. Our proposed algorithm enhances an unsupervised preliminary process known as Double Cluster Tree Structure (DCTS) whose boundary structure process handled before each iteration of FCM clustering. The combined structure of these two algorithms form Adaptive Unsupervised Fuzzy C Means (AUFCM), AUFCM analyzes and segments whole dataset (image) in an unsupervised manner. The results of this algorithm show a significant improvement in segmentation Performance.
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Author Information
  • M E Communication Systems, Karpagam College of Engineering, Coimbatore, India

  • ECE Department, Karpagam College of Engineering, Coimbatore, India

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