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dc.contributor.authorCha, Jihun
dc.contributor.authorFausett, Laurene V
dc.date.accessioned2017-10-05T13:08:33Z
dc.date.available2017-10-05T13:08:33Z
dc.date.issued1997-04-04
dc.identifier.citationCha, J., & Fausett, L. V. (1997). Comparison of three clustering algorithms and an application to color image compression. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 3077 225-235.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1743
dc.descriptionAlgorithms, Color image processing, Data processing, Neural networks, Simulationen_US
dc.description.abstractThis paper investigates a traditional clustering algorithm (K-means) and two neural networks (SOM and ART-F). The characteristics of each algorithm are illustrated by simulating geometric space data clustering. Then each algorithm is applied to image data sets to compress the size by reducing the number of colors from 256 to 16.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleComparison of three clustering algorithms and an application to color image compressionen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.271482


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