Image Processing in Collaborative Open Narrative Systems

Abstract

Open narrative approach enables the creators of multimedia content to create multi-stranded, navigable narrative environments. The viewer is able to navigate such space depending on author’s predetermined constraints, or even browse the open narrative structure arbitrarily based on their interests. This philosophy is used with great advantage in the collaborative open narrative system NARRA. The platform creates a possibility for documentary makers, journalists, activists or other artists to link their own audiovisual material to clips of other authors and finally create a navigable space of individual multimedia pieces. To help authors focus on building the narratives themselves, a set of automated tools have been proposed. Most obvious ones, as speech-to-text, are already incorporated in the system. However other, more complicated authoring tools, primarily focused on creating metadata for the media objects, are yet to be developed. Most complex of them involve an object description in media (with unrestricted motion, action or other features) and detection of near-duplicates of video content, which is the focus of our current interest. In our approach, we are trying to use motion-based features and register them across the whole clip. Using Grid Cut algorithm to segment the image, we then try to select only parts of the motion picture, that are of our interest for further processing. For the selection of suitable description methods, we are developing a meta-learning approach. This will supposedly enable automatic annotation based not only on clip similarity per se, but rather on detected objects present in the shot.