Path: Top -> Journal -> Jurnal Internasional -> King Saud University -> 2019 -> Volume 31, Issue 3, July

A fuzzy image clustering method based on an improved backtracking search optimization algorithm with an inertia weight parameter

Journal from gdlhub / 2019-09-19 10:50:22
Oleh : Guliz Toz, Ibrahim Yucedag, Pakize Erdogmus, King Saud University
Dibuat : 2019-09-19, dengan 1 file

Keyword : BSA, FCM, Image clustering
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Sumber pengambilan dokumen : WEB

In this paper, we introduced a novel image clustering method based on combination of the classical Fuzzy C-Means (FCM) algorithm and Backtracking Search optimization Algorithm (BSA). The image clustering was achieved by minimizing the objective function of FCM with BSA. In order to improve the local search ability of the new algorithm, an inertia weight parameter (w) was proposed for BSA. The improvement was accomplished by using w in the steps of the determination of the search-direction matrix of BSA and the new algorithm was named as w-BSAFCM. In order to show the effectiveness of the new algorithm, FCM was also combined with the general forms of BSA in the same manner and three benchmark images were clustered by utilizing these algorithms. The obtained results were analyzed according to the objective function and Davies-Bouldin index values to compare the performances of the algorithms. According to the results, it was shown that w-BSAFCM can be effectively be used for solving image clustering problem.

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PropertiNilai Properti
ID Publishergdlhub
OrganisasiKing Saud University
Nama KontakHerti Yani, S.Kom
AlamatJln. Jenderal Sudirman

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