由於使用K-means分群法執行圖片分割時,其運算速度效能不佳。因此,本研究提出一創新的以統計直方圖為基礎的K-means分群法來增加執行效能,並且預期達到與K-means分群法一樣的效果。此方法先使用統計直方圖來獲得圖片像素值的離散機率密度函數,以同像素的概念來做權重分割。如此,將統計直方圖和K-means分群法兩者結合在一起,以改進原始K-means分群法遭遇圖片像素太大或分群數過多時拖慢演算速度的缺點。此外,以大津法初始我們改良的K-means分群法,並且以多階的方式實現四值化分割。接著,以加州大學柏克萊分校電資學院(UC Berkeley EECS)的圖片集,執行多值化的分割,並評估原始K-means分群法與我們所提出方法分割的結果與效能。結果於實驗中以統計直方圖為基礎的K-means分群法在執行效能上顯著地比原始K-mean分群法快十倍以上。
When K-means cluster is used for image segmentation, it is very time consuming. In this preliminary study, we proposed a novel statistical histogram-based K-means clustering to increase the computational performance and to achieve the same segmented results as K-means clustering. The proposed method uses the statistical histogram to obtain probability density function of pixels of a figure and segment with weighting for the same intensity. We combine histogram and K-means clustering together to improve the shortcoming of high computational cost for K-means clustering. In addition, the histogram-based K-means clustering is initialized by means of Otsu’ method and, it achieves the four-level segmentation with a multi-level approach. Finally, the images captured from UC Berkeley EECS are used for the multi-level segmentation and the evaluation of results and performance. The results indicate that the proposed method is significantly faster than K-means clustering by more than ten times.