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Conventional multimedia annotation/retrieval systems such as NormalizedContinuous Relevance Model (NormCRM) [16] require a fully labeled training datafor a good performance. Active Learning, by determining an order for labelingthe training data, allows for a good performance even before the training datais fully annotated. In this work we propose an active learning algorithm, whichcombines a novel measure of sample uncertainty with a novel clustering-basedapproach for determining sample density and diversity and integrate it withNormCRM. The clusters are also iteratively refined to ensure both feature andlabel-level agreement among samples. We show that our approach outperformsmultiple baselines both on a recent, open character animation dataset and onthe popular TRECVID corpus at both the tasks of annotation and text-basedretrieval of videos.

An Active Learning Based Approach For Effective Video Annotation And Retrieval