ABSTRACT
The emergence of multimedia technology coupled with the rapidly expanding image and video collections on the World Wide Web have attracted significant research efforts in providing tools for effective retrieval and management of visual information. Video data is available and used in many different application domains such as security, digital library, distance learning, advertising, electronic publishing, broadcasting, interactive TV, video-ondemand entertainment, and so on. As in the old saying, “a picture is worth a thousand words.” If each video document is considered a set of still images, and the number of images in such a set might be in hundreds or thousands or even more, it’s not so hard to imagine how difficult it could be if we try to find certain information in video documents. The sheer volume of video data available nowadays presents a daunting challenge in front of researchers – How can we organize and make use of all these video documents both effectively and efficiently? How can we represent and locate meaningful information and extract knowledge from video documents? Needless to say, there’s an urgent need for tools that can help us index, annotate, browse, and search video documents. Video retrieval is based on the availability of a representation scheme of video contents and how to define such a scheme mostly depends on the indexing mechanism that we apply to the data. Apparently, it is totally impractical to index video documents manually due to the fact that it is too time consuming.
