衡量一個互動式影像分割技術的優劣，除了比較準確度之外，所花的時間也是考量的重點之一。傳統的互動式影像分割技術在分析演算法效率時，通常僅使用一組固定的預設標籤進行一次運算；然而實際操作時，使用者會在看到結果後提供新標籤，並重複修正至滿意為止，因此這些標籤實際上應分為多次輸入。在重複操作的前提下，我們認為花費的時間應分成下列三項來討論: 一、互動次數，能以較少的互動次數達到令使用者滿意的結果者為佳；二、等待時間，在兩次互動間系統的反應時間越短越好；三、標籤數量，代表使用者提供標籤所花費的時間，少者為佳。
在這篇論文中我們提出了一種以超像素分割與標籤擴散法(Superpixel-Wise Label Propagation; SLP)為基礎的架構，將產生的超像素轉換至一個特徵空間，利用使用者提供的初始標籤取得分群結果。此架構在影像中有突出的結構或許多不相連的背景區域時，能僅用少量資訊切割出令人滿意的結果，這是以往的演算法較難以達成的。另外我們針對互動時間進行改善，利用前次互動的結果作為下次互動的初始標籤，因此SLP能以相對少的互動次數、少量的標籤及等待時間取得相當不錯的結果。此外，利用GrabCut資料庫進行比較時，SLP優於其他方法達到最佳的結果。

The performance of an interactive image segmentation method can be evaluated not only by the label accuracy, but also the interaction time. Generally, the conventional approaches evaluate their execution time by a single-iteration test with a set of input labels. In practice, however, the user would input the labels multiple times until obtaining satisfactory results. In a case of the multi-iteration segmentation task, the interaction time could be divided into three parts. Firstly, the times of interactions. The total iterations of user interaction should be as less as possible. Secondly, the waiting time. The waiting time between two iterations should be short. Thirdly, the quantity of the given labels. Less information provided is the better.
In this paper, we propose an interactive segmentation method based on over-segmentation and label propagation technique. We map the superpixels to a five dimensional feature space, then get the segmentation result by propagating the given labels. The proposed “Superpixel-Wise Label Propagation” (SLP) method can handle the object with mesh or long structures by just a few given information. The interaction time is also concerned in SLP. We adopt the segmentation result as the input labels of the next iteration. It decreases not only the computational time, but also the total times of interactions. Furthermore, SLP achieves state-of-the-art of GrabCut dataset comparing with other methods.

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