Open AccessOptimized Graph cut Color Image Segmentation using Genetic Algorithm with Weighted Constraints (OGcut)
Author(s) -
Ajit Singh
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d1109.1284s219
Subject(s) - crossover , artificial intelligence , cut , image segmentation , graph , computer science , constraint (computer aided design) , segmentation , image (mathematics) , pattern recognition (psychology) , genetic algorithm , feature (linguistics) , computer vision , mathematics , algorithm , mathematical optimization , theoretical computer science , linguistics , philosophy , geometry
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.