在影像切割(image segmentation)中若是使用調色盤方式處理可以有效的減少處理複雜度，而我們做切割的目標是希望可以找出人類所能識別的區域。在本篇論文中，我們提出了一個不同於原始調色盤處理的影像切割方法可以有效的提升效果。
在我們的方法中，我們使用了模糊影像做為找出影像中主要區域顏色的線索，藉由找出這些顏色來當作調色盤上的顏色並對影像重新上色，最後使用我們專門的優化程序來對結果進行修正與優化。我們所提出的方法在自然影像的分割結果上都可以得到相當不錯的結果。

Using color palette to solve the image segmentation problem can reduce the image color complexity and the difficulty of the computation efficiency, the main challenges in the work are selecting the representative color of the palette and how to refine the result. In our color palette method (CPB) we accept the color information from the blurred image which is different from contour-guide color palette (CCP). Using mean-shift (MS) clustering to find out the key regions’ color of the image and treat these color as representative color of the palette. We use the palette to repaint the whole image and followed by the segments refinement to improve the system’s result. The refinement includes two processes: 1. Segments redefinition which refers the image’s strong edge to split and redefine the region, 2. Region merging includes three steps “strong edge category”,” color category” and the “micro region merging”, In our refinement we not only consider the color space restriction but also add the contour clue. In this way we can easily solve the common problem in image segmentation. The performances of CPB are compared and analyzed with the CCP which also utilize color palette to solve the segmentation problems. We use Berkeley Segmentation Dataset for image segmentation test. Furthermore we use Microsoft Research Asia dataset to test our result in figure-ground segmentation.

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