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Novel image segmentation using particle swarm optimization
Published in Association for Computing Machinery
Pages: 46 - 50
Data clustering and classification technique algorithms often need to possess enough and prominent number of features in the data. Repeating and dominant features are useful in clustering or segmenting the image. The image segmentation method based on k-mean clustering, hierarchical clustering, and expectation maximization derives the optimum cluster centers based on the number of features such as similar intensity region. Deriving such number optimum number of clusters and its centers is an optimization problem. The aim of this paper is to improve the image segmentation using nature inspired techniques. Image segmentation which is complex optimization problem can be solved by this simple nature inspired PSO (Particle swarm optimization) model which is formulated in this paper. PSO model is generic model which is used to solve number of scientific problems. This paper formulates simple PSO model to solve the image segmentation problem. The proposed algorithm randomly assigns the centers to swarm and best value of objective function is initialized best on the color histogram of an image. This is discussed in section 2 and 3 of paper. Section 4 and 5 discusses and results and concluding remarks on results. © 2018 Association for Computing Machinery.
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JournalData powered by TypesetACM International Conference Proceeding Series
PublisherData powered by TypesetAssociation for Computing Machinery