Within many of these types, there is a range of purposes: to inform, to entertain, and to persuade. The content reflects on its creators as well as its intended audience, their culture and value systems. Continually, creative thinkers strive to build novel experiences for audiences.
Therefore these general classes should be understood to be only approximate, to give the reader a flavor of the range of video material encountered, and to provide an appreciation of the scope of the problem domain for video search engines.
Although we can estimate production costs for these content types, estimating the value for the user is more difficult. Security footage is largely valueless except for rare instances when a criminal act is captured, and then the value can have enormous impact.
Home video content may be quite valuable for immediate family members, but of little value for anyone else. If a person gains celebrity, then video from their childhood may be of great interest for large audiences. News archives have incalculable historic value. In general terms, we may assess a searchable video collection on these merits:
1. production cost and perceived value of the content;
2. size and breadth of coverage of the content archive;
3. size of the audience interested in the content;
4. motivation for search (entertainment, research, forensic, etc.);
5. degree to which the content is accessible (on line, either open or restricted);
6. video quality (resolution, bit rate).
It is also important to consider the value of automated indexing systems, and here we draw a distinction between using media processing to derive information about the video contents and using manual methods to create this data.
Even though it may be of great value to spot a terrorist in 10,000 hours of airport security camera footage, if there are no reliable algorithms to perform this search, then we cannot realize this potential value.
Also, manually created metadata may be available to different degrees for each of these content types either via logging production data (e.g. the text of the titles typed into a consumer video editor) or by annotating postproduction information such as with Major League Baseball statistics.
For manually extracted data, a special purpose database is constructed, while a search engine must derive common tags from a wide range of content sources – and currently this metadata normalization is not a fully automated process.
High value content benefits less from automated media logging or metadata extraction while for lower production budget content, these automated methods are more valuable since manual labeling is not practical. Since the value of the content falls off at lower production costs, the center of the production cost spectrum, semi-professional or enterprise content, represents an area of opportunity for video systems research.
The amount of video content on the Web is growing rapidly as new technologies such as Internet protocol television (IPTV) and mobile video are deployed. Video search engines are being developed to enable users to take advantage of these video resources for a wide variety of applications including entertainment, education and communications. However, the task of information extraction from video for retrieval applications is challenging, providing opportunities for innovation.
All video is not created equal; there is a wide range in terms of quality, available metadata and content. We described some of the challenges for video search as related to text search, and introduced the notion that metadata plays a key role in the accuracy and effectiveness of video search. The metadata may accompany that content and be easily ingested for search, and powerful media analysis technologies may be employed to extract additional, detailed metadata for search.
Users may be participants in the metadata creation process through tagging and otherwise commenting on the video that they have viewed. Analysis of user activity can lead search engines to make implications about video content and quality or popularity.