Video Retrieval System based on Machine Learning

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Query-By-Example (QBE) can be considered as a machine learning problem. Here, given videos for a query, a classifier is built to discriminate between relevant and irrelevant videos based on features like color, edge and motion. This research has explored QBE from the perspectives of machine learning, such as training examples, features, learning algorithms and data size. As a result, we have developed a fast and accurate method which can retrieve videos relevant to a query from a large amount video data. Furthermore, by applying the developed method to object recognition, we have achieved the highest performance at TRECVID 2012 that is a NIST-sponsored annual worldwide competition on video analysis.