Automatic Segmentation of Natural Color Images in CIE Lab Space using Possibilistic Fuzzy C Means Clustering
V. Kalist1, A. Anne Frank Joe2, Y. Justindhas3, G. Vishnupriya4
1V. Kalist, Dept of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, (Tamil Nadu), India.
2A. Anne Frank Joe, Dept of Electronics and Instrumentation Engineering, Sathyabama Institute of Science and Technology, Chennai, (Tamil Nadu), India.
3G. Vishnupriya, Dept. of Computer Science and Engineering, Jeppiaar Maamallan Engineering College, Chennai, (Tamil Nadu), India.
4Y. Justindhas, Dept. of Computer Science and Engineering, Jeppiaar Maamallan Engineering College, Chennai, (Tamil Nadu), India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 06 July 2019 | Manuscript published on 30 July 2019 | PP: 3448-3452 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8464078919/19©BEIESP | DOI: 10.35940/ijitee.I8464.078919
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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: Clustering is the most significant assignment in image processing. This work performs the segmentation of natural color images in CIELab space based on the Possibilistic fuzzy c means clustering (PFCM).The basic principle of the proposed approach is the segmentation of natural color images based on the two-way approach of hill climbing (HC) and PFCM. In this work, RGB image is transformed into CIELab space for the efficient extraction of the secreted treasure in the images. The combined approach of local optimization search technique, HC and PFCM is applied for the segmentation of synthetic fiber images. This color histogram based technique works on the principle of identification of peaks in the color histogram of the natural color image. The identified peaks are considered as initial seed or clusters. These seeds are then applied to the PFCM to perform the final segmentation. Investigational outcomedemonstrates the competence of thetwo-way approach of HC and PFCM which presents the preeminentend result for less complexity color images.
Keywords: Clustering, Color Image, Segmentation, CIE Lab Color Space, PFCM
Scope of the Article: Fuzzy Logics