Synthetic Fiber Image Segmentation using a Cooperative System of Local Hill Climbing Optimization and K-Means Clustering
Ganesan P1, M. Vadivel2, V. G. Sivakumar3
1Ganesan P, ECE Dept. of, Vidya Jyothi Institute of Technology, Hyderabad, India.
2M.Vadivel, ECE Dept. of, Vidya Jyothi Institute of Technology, Hyderabad, India.
3V.G.Sivakumar, ECE Dept., Vidya Jyothi Institute of Technology, Hyderabad, India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 27 March 2019 | Manuscript published on 30 July 2019 | PP: 2512-2515 | Volume-8 Issue-2, July 2019 | Retrieval Number: A2209058119/19©BEIESP | DOI: 10.35940/ijrte.A2209.078219
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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: The proposed method explains the segmentation of synthetic fiber images based on the cooperative approach of local hill climbing and k means clustering. In this work, RGB image is transformed into CIEL ch space for the efficient extraction of the hidden treasure in the images. The combined approach of local optimization search technique, HC and KMC 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 satellite image. The identified peaks are considered as initial seed or clusters. These seeds are then applied to the KMC algorithm to perform the final segmentation. The combined approach of HC and KMC had provided the best result for less complexity images.
Index Terms: Segmentation, Hill Climbing, K-Means Clustering, Optimization.
Scope of the Article: Discrete Optimization