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This research focuses on designing a robust and flexible registration method for wide-area augmented reality applications using scene recognition and natural features tracking techniques. Instead of building a global map of the wide-area scene, we propose to partition the whole scene into several sub-maps according to the user's preference or the requirements of the augmented reality (AR) applications. Random classification trees are used to learn and recognize the reconstructed scenes because they naturally handle multi-class problems, while being both robust and fast. The result is a system that can deal with large scale scene that previous methods cannot cope with. We also propose a hybrid natural features tracking strategy combining both wide and narrow baseline techniques. While providing seamless registration, our system can recover from registration failures and switch between different sub-maps automatically. Experimental results demonstrate the validity of the proposed method for wide-area augmented reality applications.