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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15758
Title: Multispectral images segmentation using fuzzy probabilistic local cluster for unsupervised clustering
Authors: Mantilla, Luis
Yari Ramos, Yessenia Deysi
Keywords: Artificial intelligence;Classification (of information);Pattern recognition;Gaussian dispersions;Multispectral images;Objective functions;Satellite images;Similarity between objects;Unsupervised classification;Unsupervised clustering;Weight information;Image segmentation
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050374001&doi=10.1109%2fLA-CCI.2017.8285729&partnerID=40&md5=4cc30d1d91d8876342f2618ab0975d04
Abstract: In Pattern Recognition there are many algorithms it try to solve the problem of grouping objects of the same type, this is called clustering, however the task of dividing these lies not only in the objective function, but also the methodology used to calculate the similarity between objects. Because multispectral images contain information that has low statistical separation and a large amount of data it is necessary to enter local information. In this paper, the use of the Gaussian dispersion equation is proposed in order to calculate the contribution of each sample to the sample analyzed. The results show that the integration of local weights within the clustering model decreases the entropy of each group generated. © 2017 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15758
ISBN: 9781538637340
Appears in Collections:Artículos de investigación

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