Optimized Graph cut Color Image Segmentation Using Genetic Algorithm with Weighted Constraints (OGcut)
A. Robert Singh1, Suganya A2
1Dr. A. Robert Singh, Department of Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2Suganya A, Department of Computing, Sastra Deemed to be University, Thanjavur (Tamil Nadu), India.
Manuscript received on 01 December 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 31 December 2019 | PP: 583-587 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D11091284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1109.1284S219
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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: This paper proposes a Novel color image segmentation using Graph cut method by minimizing the weighted energy function. This method is applying a pair of optimal constraints namely: color constraint and gradient constraint. In the state-of-the-art methods, the background and foreground details are manually initialized and used for verifying the smoothness of the region. But in this proposed method, they are dynamically calculated from the input image. This feature of the proposed method can be used in color image segmentation where more number of unique segments exists in a single image. The genetic algorithm is applied to the graph obtained from the graph cut method. The crossover and mutation operators are applied on various subgraphs to populate the different segments.
Keywords: Graph Cut, Segmentation, Genetic Algorithm.
Scope of the Article: Image Security