Abstract
Image segmentation is a crucial step to recognizing an object. During the segmentation process, pixels in an image are categorized based on their gray color. In pixel classifications, the K-means clustering algorithm is commonly used. In this approach, the centroid of the segment was measured using arithmetic mean and Euclidean distance. In the proposed paper, the centroid was updated using the hybridization of harmonic and arithmetic means. The proposed algorithm makes use of the harmonic and arithmetic mean features. The experimental results are compared to conventional K-means and harmonic K-means algorithms, demonstrating that the proposed algorithm performs better when checking segmentation consistency.
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Kumari, R., Gupta, N. (2022). Segmentation of Image Using Hybrid K-means Algorithm. In: Agrawal, D.P., Nedjah, N., Gupta, B.B., Martinez Perez, G. (eds) Cyber Security, Privacy and Networking. Lecture Notes in Networks and Systems, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-16-8664-1_32
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DOI: https://doi.org/10.1007/978-981-16-8664-1_32
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