An Active Learning Based Approach For Effective Video Annotation And Retrieval

2015/04/27 by Chatterjee, Moitreya, Leuski, Anton
#FOS: Computer and information sciences #H.3.3 #H.5.1 #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Multimedia (cs.MM)

paper · doi:10.48550/arxiv.1504.07004

Abstract

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.

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