Initialization Methods For Conventional Fuzzy C-Means And Its Application Towards Colour Image Segmentation
Due to its capability in providing a particularly promising solution to clustering problems, the conventional Fuzzy C-Mean (FCM) algorithm is widely used as a segmentation method. However, it is very sensitive to the initialization conditions of number of clusters and initial cluster centres. Thus,...
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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2011
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| 主題: | |
| 在線閱讀: | http://eprints.usm.my/42775/1/TAN_KHANG_SIANG.pdf |
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| 總結: | Due to its capability in providing a particularly promising solution to clustering problems, the conventional Fuzzy C-Mean (FCM) algorithm is widely used as a segmentation method. However, it is very sensitive to the initialization conditions of number of clusters and initial cluster centres. Thus, three initialization schemes for the conventional FCM algorithm namely the Hierarchical Approach (HA), the Colour Quantization (CQ) and the Histogram Thresholding (HT) are proposed to automatically obtain the initialization conditions for the conventional FCM algorithm. |
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