Content-based digital video retrieval

All video will eventually become fully digital. Consequently, digital video databases will become more and more pervasive and finding video in large digital video databases will become a problem just like it is a problem today to find video in analog video databases. The digital form of the video, however, opens up tremendous possibilities. Just like it is possible today to retrieve text documents from large text document databases by querying document content represented by indices, it will become possible to index digital video databases based on (semi-)automatically derived indices. In this paper, we address the problem of automatic video annotation-associating semantic meaning with video segments to aid in content-based video retrieval. We present a novel framework of structural video analysis which focuses on the processing of low-level visual data cues to obtain high-level (structural and semantic) video interpretations. Additionally, we propose a flexible framework for video query formulation to aid rapid retrieval of video. This framework is meant to accommodate the “depth-first searcher”-i.e. the power user, the “breath-first searcher”, and the casual browser