SemanticAd: A Multimodal Contextual Advertisement Framework for Online Video Streaming Platforms

Abstract

In the past few years, the online video streaming market has witnessed rapid growth and has become the most important form of entertainment. Motivated by the huge business opportunities, the advertisement insertion mechanisms have become a hot topic of research and represent the most important component of an online delivery ecosystem. In this paper, we introduce SemanticAd, a multimodal ad insertion framework designed from the viewers’ perspective in terms of the quality of experience and degree of intrusiveness. The core of the proposed approach involves a novel temporal segmentation algorithm that extracts story units with a frame level precision. To the best of our knowledge, the proposed solution is the most robust and accurate solution dedicated to TV news videos. In addition, by taking into consideration ad temporal distribution and semantic information, the framework proposes commercials that are contextually relevant with respect to video content. The quantitative and qualitative experimental results conducted on a challenging set of 50 multimedia documents validate the SemanticAd methodology, returning a F1-score superior to 92%. Moreover, when compared to other state-of-the-art methods, our system demonstrates its superiority with gains in performance ranging in the [4.19%, 10.22%] interval.

Keywords

• Content targeted ad insertion
• multimodal video analysis
• story unit extraction
• video temporal segmentation