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

Abstract

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.