FCM Algorithm: Analysis of the Membership Function Influence and Its consequences for fuzzy clustering

被引：0

作者：

Mantilla, Luis ^{[1
]}

Yari, Yessenia ^{[2
]}

机构：

[1] Univ Catolica Trujillo Benedicto XVI, Trujillo, Peru

[2] Univ Catolica San Pablo, Arequipa, Peru

来源：

2020 IEEE COLOMBIAN CONFERENCE ON APPLICATIONS OF COMPUTATIONAL INTELLIGENCE (IEEE COLCACI 2020) | 2020年

关键词：

Satellite image; clustering; segmentation; Fuzzy C-Means; fuzzy clustering; SEGMENTATION;

D O I：

10.1109/colcaci50549.2020.9247944

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Image segmentation in satellite images is a task widely investigated since we can extract some information of an image and analyze it. We propose to use a weighted factor for each of the distances used to calculate the degree of membership of each element to the cluster. In this way, we seek to reduce the influence of the upper and the lower bounds on the FCM equation. This paper reports preliminary results of the experiments and shows that the proposed algorithm performs accurately on a real dataset. For the evaluation of the algorithm, different cluster validity indexes are employed.

引用

页数：7

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