While it would seem that digital video libraries should benefit from access mechanisms directed to their visual contents, years of TREC Video Retrieval Evaluation (TRECVID) research have shown that text search against transcript narrative text provides almost all the retrieval capability, even with visually oriented generic topics. A within-subjects study involving 24 novice participants on TRECVID 2004 tasks again confirms this result. The study shows that satisfaction is greater and performance is significantly better on specific and generic information retrieval tasks from news broadcasts when transcripts are available for search. Additional runs with 7 expert users reveal different novice and expert interaction patterns with the video library interface, helping explain the novices' lack of success with image search and visual feature browsing for visual information needs. Analysis of TRECVID visual features well suited for particular tasks provides additional insights into the role of automated feature classification for digital image and video libraries.

Abstract. This paper investigates the applicability of Informedia shot-based interface features for video retrieval in the hands of novice users, noted in past work as being too reliant on text search. The Informedia interface was redesigned to better promote the availability of additional video access mechanisms, and tested with TRECVID 2005 interactive search tasks. A transaction log analysis from 24 novice users shows a dramatic increase in the use of color search and shot-browsing mechanisms beyond traditional text search. In addition, a within-subjects study examined the employment of user activity mining to suppress shots previously seen. This strategy did not have the expected positive effect on performance. User activity mining and shot suppression did produce a broader shot space to be explored and resulted in more unique answer shots being discovered. Implications for shot suppression in video retrieval information exploration interfaces are discussed.

Current NASA plans envision human beings returning to the Moon in 2018 and, once there, establishing a permanent outpost from which we may initiate a long-term effort to visit other planetary bodies in the Solar System. This will be a bold, risky, and costly journey, comparable to the Great Navigations of the fifteenth and sixteenth centuries. Therefore, it is important that all possible actions be taken to maximize the astronauts' safety and productivity. This can be achieved by deploying fleets of autonomous robots for mineral prospecting and mining, habitat construction, fuel production, inspection and maintenance, etc.; and by providing the humans with the capability to telesupervise the robots' operation and to teleoperate them whenever necessary or appropriate, all from a safe, "shirtsleeve" environment. This paper describes the authors' work in progress on the development of a Robot Supervision Architecture (RSA) for safe and efficient space exploration and operation. By combining the humans' advanced reasoning capabilities with the robots' suitability for harsh space environments, we will demonstrate significant productivity gains while reducing the amount of weight that must be lifted from Earth -and, therefore, cost.Our first instantiation of the RSA is a wide-area mineral prospecting task, where a fleet of robots survey a pre-determined area autonomously, sampling for minerals of interest. When the robots require assistance -e.g., when they encounter navigation problems, reach a prospecting site, or find a potentially interesting rock formationthey signal a human telesupervisor at base, who intervenes via a high-fidelity geometrically-correct stereoscopic telepresence system (Figure 1a). In addition to prospecting, the RSA applies to a variety of other tasks, both on the surface: mining, transporting, and construction -and on-orbit: construction, inspection, and repair of large space structures and satellites (Figure 1b).