Abstract
In image analysis, image segmentation performed an essential role to get detail information about image. Image segmentation is suitable in many applications like medicinal, face recognition, pattern recognition, machine vision, computer vision, video surveillance, crop infection detection and geographical entity detection in map. FCM is famous method used in fuzzy clustering to improve result of image segmentation. FCM doesn’t work properly in noisy and nonlinear separable image, to overcome this drawback, Multi kernel function is used to convert nonlinear separable data into linear separable data and high dimensional data and then apply FCM on this data. NMKFCM method incorporates neighborhood pixel information into objective function and improves result of image segmentation. New proposed method used RBF kernel function into objective function. RBF function is used for similarity measure. New proposed algorithm is effective and efficient than other fuzzy clustering algorithms and it has better performance in noisy and noiseless images. In noisy image, find automatically required number of cluster with the help of Hill-climbing algorithm.
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References
Yu, C.-Y., Li, Y., Liu, A.L., Liu, J.H.: A novel modified kernel fuzzy c-means clustering algorithms on image segmentation. In: 14th IEEE International Conference (2011). ISSN 978-0-7695-4477
Chan, S., Zhang, D.: Robust image segmentation using FCM with spatial constraints based on new kernel - induced distance measure. IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 34(4), 1907–1916 (2004)
Zanaty, E., Aljahdali, S.: Improving fuzzy algorithms for automatic magnetic resonance image segmentation. Int. Arab J. Inf. Technol. 7(3), 271–279 (2009)
Kaur, P., Gupta, P., Sharma, P.: Review and comparison of kernel based fuzzy image segmentation techniques. Int. J. Intell. Syst. Appl. 7, 50–60 (2012)
Islam, S., Ahmed, M.: Implementation of image segmentation for natural images using clustering methods. IJETAE 3(3), 175–180 (2013). ISSN 2250-2459, ISO 9001:2008 Certified Journal
Cannon, R.L., Dave, J.V., Bezdek, J.C.: Efficient implementation of the fuzzy C –means clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(2), 248–255 (1986)
Hofman, M.: Support vector Machines-Kernel and the Kernel Trick, pp. 1–16 (2006)
Zang, D., Chen, S.: A novel kernalized fuzzy C-means algorithm with application in medical image segmentation. Artif. Intell. Med. 32, 37–50 (2004)
Zanaty, E.A., Aljahdli, S., Debnath, N.: A kernalized fuzzy C-means algorithm for automatic magnetic resonance image segmentation. J. Comput. Methods Sci. Eng. Arch. 9(1, 2S2), 123–136 (2009)
Kochra, S., Joshi, S.: Study on hill-climbing algorithm for image segmentation technology. Int. J. Eng. Res. Appl. (IJERA) 2(3), 2171–2174 (2012). ISSN 2248-9622
Paithane, P.M., Kinariwala, S.A.: Automatic determination number of cluster for NMKFC-means algorithms on image segmentation. IOSR J. Comput. Eng. (IOSR-JCE) 17, 12–19 (2015)
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Paithane, P.M., Kakarwal, S.N. (2020). Automatic Determination Number of Cluster for Multi Kernel NMKFCM Algorithm on Image Segmentation. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_85
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DOI: https://doi.org/10.1007/978-3-030-16660-1_85
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