Video instance search for embedded marketing

With the rise of online sharing platforms such as YouTube
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, advertisers become more interested in providing relevant advertisements (ads) when the embedded products are presented in videos during broadcast, so that the number of hits and potential customers will be increased. Given the product image of interest, we present a framework which allows the advertisers or video deliverers to automatically detect the embedded products throughout the video, so that relevant ads or latest product information can be delivered to the viewers accordingly. We advance the boundary preserving dense local regions (BPLR) as the local descriptors for the query and each video frame, and utilize different types of features to describe the local region. To make our framework robust yet efficient, we reduce the search space by applying the technique of inverted index, and we propose a probabilistic framework to identify the video frames in which the product of interest is presented. Experiments on TRECVID, commercial, and movie datasets confirm the effectiveness of our proposed framework.