影像分割是影像處理和電腦視覺領域的一個重要議題。由於影像包含了複雜的資訊，有效率地分割出前景物體是具挑戰性的問題。近來許多方法都利用圖形切割進行最佳化，因為它能成功地結合顏色和邊緣資訊。然而分割的結果相當依賴選用的種子，要自動地取得可靠的種子是十分困難的。為了解決這個問題，我們提出一個自動式的影像分割架構。相較於傳統的雙標籤圖形切割，多標籤圖形切割的結果較不依賴選用的種子。我們的方法利用多標籤圖形切割將影像分割成多個區塊，接著再將這些區塊分類為物體和背景。其中還引用了標準差來調整各項屬性的重要性。實驗顯示提出的方法產生了較過去自動式方法更準確的分割結果，而且比得上互動式方法。

Image segmentation is an important issue in image editing and computer vision. Due to the complexity of information in images, efficient extraction of a foreground object is a challenging problem. Recently, several approaches based on optimization by graph cuts have been developed which successfully combine the color feature with the edge information. A problem is that the segmentation results heavily depend on the seeds selection. However, it is difficult to obtaining reliable seeds automatically. To overcome this problem, we propose an automatic scheme for image segmentation. Compare to the classical binary-label graph cuts, the results by the multi-label graph cuts do not heavily depend on the seeds selection. Our method uses the multi-label graph cuts to separate an image into multiple segments, and then classify the segments into the object and the background. We introduce the standard deviation to adapt the importance between the properties in our method. Experiments show that the proposed method yields more accurate segmentation results than the previous automatic approach and is comparable to the interactive approach.

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