A fuzzy image clustering method based on an improved backtracking search optimization algorithm with an inertia weight parameter
| Autor: | Pakize Erdogmus, İbrahim Yücedağ, Guliz Toz |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
BSA
General Computer Science Computer science media_common.quotation_subject Physics::Medical Physics ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Inertia Fuzzy logic lcsh:QA75.5-76.95 Image (mathematics) Matrix (mathematics) 0202 electrical engineering electronic engineering information engineering Local search (optimization) Cluster analysis media_common FCM Backtracking business.industry 020206 networking & telecommunications Image clustering ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Benchmark (computing) 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science business Algorithm |
| Zdroj: | Journal of King Saud University: Computer and Information Sciences, Vol 31, Iss 3, Pp 295-303 (2019) |
| ISSN: | 1319-1578 |
| DOI: | 10.1016/j.jksuci.2018.02.011 |
| Popis: | WOS: 000474400800003 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. (C) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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