An automatic fuzzy clustering segmentation algorithm with aid of set partitioning

Yanling Li, Zhiwei Gao, Xiaoxu Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As one of the most popular methods for image segmentation, fuzzy C-means algorithm suffers two unavoidable initialization difficulties including obtaining initial cluster centroids and deciding cluster number, which affect the algorithm performance. Motivated by the above, an automatic fuzzy clustering algorithm is proposed in this paper, where observation matrix, judgment matrix and set partitioning are used to select appropriate clustering number automatically. Experimental results show that automatic fuzzy clustering algorithm not only can spontaneously estimate the appropriate number of clusters but also can achieve better segmentation quality.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages647-652
Number of pages6
ISBN (Electronic)9781538608371
DOIs
Publication statusPublished - 10 Nov 2017
Event15th IEEE International Conference on Industrial Informatics, INDIN 2017 - Emden, Germany
Duration: 24 Jul 201726 Jul 2017

Publication series

NameProceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017

Conference

Conference15th IEEE International Conference on Industrial Informatics, INDIN 2017
CountryGermany
CityEmden
Period24/07/1726/07/17

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