Advancing Content-Based Retrieval Effectiveness with Cluster-Temporal Browsing in Multilingual Video Databases

Interactive experiments on video retrieval systems need to address the problem of internal validity, i.e. how much the test users´ experience affects the retrieval effectiveness. This paper compares the semantic retrieval performance of novice users and expert system developers. The test system utilizes cluster-temporal browsing, which combines chronological video structure and computation of similarities into single interface. Interactive experiments with eight test users were carried out in a database of ~80 hours of multilingual news video from TRECVID 2005 benchmark. A cluster-temporal browser was found to improve the retrieval effectiveness by 12% with novice system users. Expert users were able to achieve 18% better performance than the novice users. Additionally, manual search experiments demonstrated that search performance can be improved by 19-25% when a plain text search is supplemented with content-based features