影像切割(image segmentation)問題的目標通常是把一張輸入影像切割成許多不同的區域。大致上而言，影像切割的問題通常可視為標籤決定問題並且根據每一個像素的特徵標示不同的標籤。在此篇論文中，我們會提出一個監督式和互動式的影像切割演算法。
在我們的方法中，我們建構一個由超向素(superpixel)層與高階層所組成的圖解模型。超像素層是由過度切割區域的區域稱為超像素所組成的，高階層則是由邊緣偵測的結果與過度切割的區域所構成的。接下來我們使用建構的圖解模型並且使用隨機漫步演算法來找出每個超像素最大機率的標籤值。我們所提出的方法在自然影像中跟其他常見方法比較下有非常滿意的結果。

The purpose of image segmentation problem is to separate some areas from the input image. In general, image segmentation can be consider as a label decision problem which assign different labels to every pixel according to its features. In this paper, we propose a supervised and interactive image segmentation algorithm.
In our approach, we construct a new graph model which consists of a super-pixel layer and a high order layer. The super-pixel layer is composed by over-segmentation regions called superpixels and the high-order layer is generated by combining edge detection and these over-segmentation regions. Then we construct a graph model and use a random walk algorithm to find the maximum probability label value for each superpixel. The proposed method shows very satisfactory results for some natural images and compares to some conventional methods.

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