影像共分割的概念是在物件辨識上用於增加其物件準確度，其定義為將一組具有共同物件的影像同步分割出相似的部分，利用影像間互相做參考得到共同物件，補足影像分割只能定義相同材質的區域但卻不能定義出物件的缺點，其影像共分割概念近年來被廣泛討論與研究中。
本篇論文中，我們認為在每張影像的前處理中只要能先分割出較適當的區域，在之後影像共分割參考時必能得到更好的結果。一開始我們將每張影像切成若干個superpixels，抽取其顏色直方圖(histogram)特徵，接著利用階層式群集(hierarchical clustering)的概念，在每次迭代將最相似的superpixel融合直到適當之門檻值為止，此舉不但確保融合的superpixel間具有相同的材質，且因為superpixel的數量減少所以對於之後影像共分割中也有效減少計算量。另外在融合中我們會記錄每次之融合相對距離並保留最大值，此值可用於之後影像共分割作為一可配對之門檻值，給予一範圍以增加其配對之正確率。接著我們利用GrowCut的方法來完成分割，成功分出我們所要的理想結果。研究結果證明我們的方法不僅可以達到比較好的結果，對於此篇論文中我們也免除了預設值之設定，自動化產生結果，對於使用者來說也是一個非常好的辦法。

"Co-segmentation" can increase the accuracy of object recognition. The concept of co-segmentation is the problem of simultaneously dividing multiple images into common object, reference each other to segment similar region as an object. In recent years, the problem of image co-segmentation has been widely discussed.
In our paper, we believe that each image pre-processing can be divided to many appropriate segments, and then co-segmentation will get the better results. At beginning, we segment each image into number of suerpixels, and extract their color histogram features. And we follow the concept of hierarchical clustering, we merge pair of superpixels which most similar with each other into one superpixel in each iteration until the appropriate threshold. This is not only ensure the superpixels which merge together have same material, but also effective in reducing the amount of computation. In addition, each superpixel records the maximum relative distance. The value can be used as a range to increase the accuracy of our co-matching method. Finally, we use GrowCut to get the final result. The results show that our method can not only achieve better results, but also we don’t need to add any setting, it is a good way for user that produces results automatically.

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