ActiveAd: A novel framework of linking ad videos to online products

With the wide use of consumer electronics and the rapid development of online shopping, more and more ad videos are developed for IDTV and mobile users. However, a huge amount of time spending on the Internet advertising somehow brings users uncomfortable viewing experience rather than effectively generates high consumption of advertised products. Therefore, it is urgent to find a viewer-friendly and advertiser-beneficial solution to launch ads. This paper is the first attempt to improve the effectiveness of advertising through combining online shopping information with an ad video and directing viewers to proper online shopping places. The proposed ActiveAd framework includes four main components. Firstly, an ad video analysis component detects both syntactic and semantic elements from ad videos, e.g. FMPIs (Frame Marked with Production Information), visual concepts, and textual keywords within the ad videos. Our ad video analysis provides a comprehensive solution to extract meaningful elements from ad videos. Secondly, a visual linking by search component is proposed to collect websites which contain similar images as FMPIs. Features used for the visual search are weighted and fused in order to ensure the uniformity of search results. Thirdly, different kinds of tags and product categories extracted from collected websites are aggregated in order to identify the representative text of the product. Finally, query keywords are selected through calculating cosine similarity from two kinds of keywords, i.e. keywords identified from tag aggregation and keywords obtained through ad video analysis (visual concept detection and textual keyword detection). A query vector is generated by selected keywords and used to retrieve product online. In this paper, a powerful cross-media contextual search including visual search, tag aggregation and textual search is achieved with the help of ad video analysis. Experiments demonstrate that our proposed ActiveAd achieves product recommendation effectively and efficiently.