Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation
Science Journal of Circuits, Systems and Signal Processing
Volume 3, Issue 6-1, December 2014, Pages: 1-5
Received: Sep. 17, 2014; Accepted: Sep. 22, 2014; Published: Oct. 20, 2014
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Arunkumar Rajendran, M E Communication Systems, Karpagam College of Engineering, Coimbatore, India
Thamarai Muthusamy, ECE Department, Karpagam College of Engineering, Coimbatore, India
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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.
Fuzzy C Means, Image Segmentation, Unsupervised Learning, AUFCM and DCTS
To cite this article
Arunkumar Rajendran, Thamarai Muthusamy, Adaptive Unsupervised Fuzzy C Mean Based Image Segmentation, Science Journal of Circuits, Systems and Signal Processing. Special Issue: Computational Intelligence in Digital Image Processing. Vol. 3, No. 6-1, 2014, pp. 1-5. doi: 10.11648/j.cssp.s.2014030601.11
D. Wang, N.M. Kwok, X. Jia and G. Fang, “A Cellular Automata Approach for Superpixel Segmentation”, 2011 4th International Congress on Image and Signal Processing.
SteliosKrinidis and VassiliosChatzis.” A Robust Fuzzy Local Information C-Means ClusteringAlgorithm”, IEEE Transactions on image processing, vol. 19, no. 5, may 2010
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.
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
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
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.
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
SaminaNaz, HammadMajeed, HumayunIrshad,” Image Segmentation using Fuzzy Clustering: A Survey”, IEEE Transaction 2010 6th International Conference on Emerging Technologies.
KusumBharti, SanyamShukla, Shweta Jain,” Intrusion Detection using Unsupervised Learning”, International Journal on Computer Science and Engineering Vol. 02, No. 05, 2010.
Satish R. Kolhe, Ranjana S. Zinjore,” Clustering Iris Data using Supervised and Unsupervised Learning”, International Journal of Computer Science and Application Issue 2010.
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].
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)
FasahatUllahSiddiqui and NorAshidi Mat Isa, “Enhanced Moving K-Means (EMKM) Algorithm for Image Segmentation”, IEEE Transaction [Volume:57, Issue: 2,May 2011].
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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