An Active Learning Based Approach For Effective Video Annotation And Retrieval
This work addresses the need for efficient video annotation and retrieval systems, offering an incremental improvement over existing methods like NormCRM.
The paper tackles the problem of reducing annotation effort for video retrieval by proposing an active learning algorithm that combines uncertainty, density, and diversity measures with iterative clustering. The result shows that this approach outperforms multiple baselines on character animation and TRECVID datasets for both annotation and retrieval tasks.
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.