Abstract
In image processing, clustering algorithms applied to the segmentation of images. Image segmentation is the practice of clustering a complete image into many meaningful non-overlapped clusters. This is a vital step in computer vision and data analytics because the result of the segmentation process has an impact on other subsequent processes. In image processing, distance is expressed as distance in pixels or shortest path between two data points on the grid, two centers of pixels. Most clustering algorithms utilize distance measures to cluster alike data points (pixels in the case of image) into the same group, while unalike data points are clustered into different groups according to image attributes. The proposed work evaluates the efficiency of the K-means clustering with three distinct distance measures.
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Ganesan, P., Sathish, B.S., Leo Joseph, L.M.I., Subramanian, K.M., Murugesan, R. (2021). The Impact of Distance Measures in K-Means Clustering Algorithm for Natural Color Images. In: Chiplunkar, N.N., Fukao, T. (eds) Advances in Artificial Intelligence and Data Engineering. AIDE 2019. Advances in Intelligent Systems and Computing, vol 1133. Springer, Singapore. https://doi.org/10.1007/978-981-15-3514-7_71
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DOI: https://doi.org/10.1007/978-981-15-3514-7_71
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