近年來，隨著數位科技的進步，許多人利用數位相機記錄每天的生活，也因此一個人在一年中可以產生數千張的數位相片，所以整理數位相片變成了一個很重要的課題。在相片分群的這個領域中，最主要的難題就是人所認知的相似性和電腦經過特徵抽取找出的相似性有差距。在這篇論文中，我們提出了一個影像分群的方式，在特徵點的抽取上結合了Local Feature 和Global Feature 的特性，我們使用的是SIFT feature 以及CLD feature。因為一張影像可以找出多個SIFT
feature，但是同時也可能找到許多屬於雜訊的點，所以我們先將SIFT feature經過分群的過程過濾掉較不重要的feature，再將這些重要的feature 組合成一個global feature，但是SIFT feature 是不包含顏色資訊的，在論文中我們使用CLD feature 來補足這個缺陷。而在影像分群的步驟中，我們採用affinity propagation 做為分群的演算法，會使用這個分群方式是因為它不需要先指定分群的數目這個特性，應用在實際的相片分群中，事先我們並無法確切掌握應該要分類的群數，然而因為有一些影像會被分到錯誤的群體中，所以我們實做bag of visual word model，並挑選可能分錯的影像出來，利用新的feature 表示法，找出最相似的群重新加入，完成整個影像分群的過程。實驗結果證實，我們提出的方法可以有效並準確的對影像分群，在處理1200 張的影像時，可以有90%的分群正確率，而當資料庫的數量大到3000 張時仍然有83%的正確率。

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