Effective and Efficient Content Redundancy Detection of Web Videos

Currently, an unprecedentedly vast amount of videos are hosted on the Internet and shared by users across the world. Within these videos, a considerable portion is duplicate or near-duplicate. Consequently, building an effective yet efficient content-based redundancy detection system is of importance, as this research would be beneficial to a variety of applications. Despite the progress in this field, designing a practical detection system for web videos continues to be difficult, because of the contradictions between the accuracy and speed requirements. In this paper, we propose a novel near-duplicate video detection system, CompoundEyes, whose design philosophy deviates from the conventional feature-centered paradigm. Instead, the focus of our system has been shifted from the design of an advanced feature representation to the design of system architecture. This design methodology not only ensures a decent detection accuracy by the collaboration of the classifiers but also substantially accelerates the detection speed due to the low dimensionality of the feature representations and the exploitation of the parallelism among the components. Experiments have been conducted to demonstrate that the CompoundEyes is both accurate and fast.