A genetic model for video content based retrieval

Recent progress in compression technology has made it possible for computer to store efficiently pictures, audio and even video. Nevertheless, if such media are widely used in today's communication, efficient computer exploitation is still lacking because automated, high-level interpretation is nowadays impossible. While storing raw video streams in analogic or digital formats is possible, to archive efficiently video documents in order to enable efficient retrieval implies being able to store and manage video content (e.g., raw features, human interpretation of the video document). Recently, this field has received a particular attention. In the past years, two approaches have been considered: (1) systems that use only automated feature extraction, like Jacob [4], and (2) more elaborated systems for concrete applications [3] [1] [5] that integrate user specifications in their video data model.