A framework for associated news story retrieval

Abstract

Video retrieval-searching and retrieving videos relevant to a given query-is one of the most popular topics in both real life applications and multimedia research. Finding relevant video content is important for producers of television news, documentaries and commercials. Particularly, in news domain, hundreds of news stories in many different languages are being published everyday by the numerous news agencies and media houses. The huge number of published news stories brings enormous challenges in developing techniques for their efficient retrieval. In particular, there is the challenge of identifying two news clips that discuss the same story. Here, the visual information need not be similar enough for simple near-duplicate video detection algorithms to work. Although, visually two news stories might be different, they might be addressing the same main topic. We call such news stories as associated new stories and the main objective in this thesis is to identify such stories. Therefore, it is imperative that we resort to other modalities such as speech and text for robust retrieval of associated news stories. In the visual domain, associated news stories can be seen as duplicate, near-duplicate, partially near-duplicate videos or in more challenging Contents List of Figures viii List of Tables xi LIST OF FIGURES