Composite Fuzzy C Means Image Segmentation
1S.Bharathi, Electrical and Electronics Department, Sree Chandra Sekarendra Saraswathi Viswa Maha Vidyalaya University, Kanchipuram, India.
2P.Venkatesani, Electronics and Communication Department, Sree Chandra Sekarendra Saraswathi Viswa Maha Vidyalaya University, Kanchipuram, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4738-4738 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4356119119/2019©BEIESP | DOI: 10.35940/ijitee.A4356.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper synthetic image segmentation is carried out by Possibilistic Rough kernel intuionistic Fuzzy c means (PRIKFCM) technique which is proposed in this paper. The results obtained through the proposed method is compared with FCM, PFCM, RKFCM segmentation techniques. The segmentation accuracy is more than the segmentation accuracy yielded by other methods. PRIKFCM is a hybrid technique incorporate in it the concepts of kernelized distance, rough ses, possibilistic and intuionistic concepts in it.the advantage of the proposed method is more due to the hybridization approach.
Keywords: PCM – Possibilistic c means Clustering, PFCM-Possibilistic Fuzzy c Means Clustering, RCM-Rough c means clustering.
Scope of the Article: Clustering