1 research outputs found
An Active Learning Based Approach For Effective Video Annotation And Retrieval
Conventional multimedia annotation/retrieval systems such as Normalized
Continuous Relevance Model (NormCRM) [16] require a fully labeled training data
for a good performance. Active Learning, by determining an order for labeling
the training data, allows for a good performance even before the training data
is fully annotated. In this work we propose an active learning algorithm, which
combines a novel measure of sample uncertainty with a novel clustering-based
approach for determining sample density and diversity and integrate it with
NormCRM. The clusters are also iteratively refined to ensure both feature and
label-level agreement among samples. We show that our approach outperforms
multiple baselines both on a recent, open character animation dataset and on
the popular TRECVID corpus at both the tasks of annotation and text-based
retrieval of videos.Comment: 5 pages, 3 figures, Compressed version published at ACM ICMR 201