影像分割技術在影像處理以及電腦視覺中一直都是一個重要的問題，它分為兩類：監督式分割方法以及非監督式分割方法。監督式分割方法需要使用者的一些互動才能得到結果，而這樣並不便利。
近來大部份的影像分割方法都會使用顏色差異或是直方圖等來當像素及超像素間的相似度，而每種相似度的計算方法都會有其各自的弱點，例如只基於邊緣的方法沒有辦法很好的分割草叢。
在此篇論文當中，我們提出一個非監督的影像分割方法，它使用了散焦圖、邊緣以及顏色做為超像素間的相似度，我們由此生成一個邊緣強度圖，並以最小生成樹對超像素進行分群來將影像分割成前景和背景。在我們的實驗中，我們所提出的方法不需要使用者的介入，而結果也比先前的超像素群組方法要更好。

Image segmentation is an important and difficult issue in computer vision and image processing. It categorized into two categories, supervised image segmentation and unsupervised image segmentation. The supervised methods need some interactions of users. It makes those methods inconvenient. Recently, most of segmentation methods usually use similarity which is defined by color difference or histogram. Every similarity has its weak side. In this paper, we proposed an unsupervised method. It uses defocus map, edge and color as similarity of pixels or superpixels. We generate an edge strength map. Then, we construct a minimum spanning tree with the superpixels and the edge map to divide the image to foreground and background. In our experiment, out method doesn’t need user interaction and the performance is better than previous superpixels grouping method.

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