MuLVAT: A Video Annotation Tool Based on XML-Dictionaries and Shot Clustering

Recent advances in digital video technology have resulted in an explosion of digital video data which are available through the Web or in private repositories. Efficient searching in these repositories created the need of semantic labeling of video data at various levels of granularity, i.e., movie, scene, shot, keyframe, video object, etc. Through multilevel labeling video content is appropriately indexed, allowing access from various modalities and for a variety of applications. However, despite the huge efforts for automatic video annotation human intervention is the only way for reliable semantic video annotation. Manual video annotation is an extremely laborious process and efficient tools developed for this purpose can make, in many cases, the true difference. In this paper we present a video annotation tool, which uses structured knowledge, in the form of XML dictionaries, combined with a hierarchical classification scheme to attach semantic labels to video segments at various level of granularity. Video segmentation is supported through the use of an efficient shot detection algorithm; while shots are combined into scenes through clustering with the aid of a Genetic Algorithm scheme. Finally, XML dictionary creation and editing tools are available during annotation allowing the user to always use the semantic label she/he wishes instead of the automatically created ones.