AdImage: video advertising by image matching and ad scheduling optimization

With the prevalence of recording devices and the ease of media sharing, consumers are embracing huge amounts of Internet videos. There arise the needs for effective video advertisement systems following their phenomenal success in text. We propose a novel advertising system, AdImage, which automatically associates relevant ads by matching characteristic images, referred to as adImages (analogous to adWords) here. The proposed image matching method is invariant to certain distortions commonly observed in shared videos. AdImage also avoids the pitfalls of poor tagging qualities in shared videos and provides a brand-new venue to specify ad targets by image objects. Moreover, we formulate the image matching scores and the parameterized bidding information as a nonlinear optimization problem for maximizing the system revenues and user perception.