Main character detection in news and movie content

Advances in multimedia compression standards, data storage, digital hardware technology and network performance have led to a considerable increase in the amount of digital content being archived and made available online. As a result, data organization, representation and efficient search and retrieval from digital video repositories has seen increased interest from the research community in recent years. In order to facilitate access to desired media segments, many indexing techniques have been employed. Automatic content structuring is one enabling technology used to aid browse/ retrieval. Scene-level analysis and sports summarization are two examples of active research in this area. Content structuring can be considered as the task of building an ’’index” and/or ’’table of contents” for events or objects that occur throughout a programme. Our approach to content structuring is to build an index based on the reappearance of the main characters within the content. For news programmes, this can be used for temporal segmentation into individual news stories based on the fact that the anchorperson, the main ’’character” in this scenario signals the beginning of a news item. For movie content, this could provide enhanced random access browsing functionality to the end user. In this thesis we propose an approach to news story segmentation that uses low-level features and three different algorithms for temporal segmentation. We then extend this system to perform anchor-person detection using automatic face detection and clustering algorithms. An extensive manually marked up test set has been used to validate each component of our overall approach. Finally, we discuss how our approach could be extended to identify the main characters in movie content using similar classification techniques and directorial conventions.