影像去背(背景相減法)被許多多媒體應用程式使用，例如交通監測、影像監視、物體追蹤等。影像去背方法已有許多研究，各方法在不同應用上有不同優勢。雲端計算的出現使得合併不同影像去背技術和處理大量的影像處理可行性提高。因此，發展一個整合影像去背的演算法是必須的。
在本篇論文，我們實現與分析一整合影像去背演算法。應用AdaBoost演算法結合弱分類器: 基於像素的影像去背方法、基於像素區塊的影像去背方法等不同方法。在訓練過後，程式會調整每個弱分類器的權重。此程式藉由Hadoop 雲端運算架構加速。利用MapReduce架構，工作可平行運作在許多電腦上而降低運算時間。當程式完成工作時，使用者可以在使用者介面看到影像結合的結果，然後選擇較好的一張結果圖。本系統取得使用者回饋之後，會自動調整合併機制。

Background subtraction is widely used in multimedia applications, such as traffic monitoring, video surveillance, and object tracking. Because background subtraction methods have long been the subject of research, several methods which have different advantages in different applications, have been proposed. The advent of cloud computing has made possible the combination of various background subtraction techniques and the processing of large amounts of images; therefore, developing an integrated algorithm for background subtraction is necessary.
In this thesis, an integrated algorithm for background subtraction is implemented and analyzed. The proposed AdaBoost algorithm combined weak classifiers: pixel-based background subtraction methods, and block-based background subtraction methods. After training, the program adjusts the weight of each weak classifier. The program is accelerated using Hadoop cloud-computing architecture. Using a MapReduce framework, this work can be parallelized on many computers, thus reducing computing time. When the program completes its task, the user can see the combined results on the user interface and can subsequently choose the preferred result. The system can obtain user feedback and tune the combination mechanism.

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