Multi-class Active Learning for Video Semantic Feature Extraction

Active learning has been demonstrated to be a useful tool to reduce human labeling effort for many multimedia applications, especially for those handling large video collections. However, most of the previous work on active learning has focused on only binary classification, which greatly limits the applicability of active learning. We present a multi-class active learning approach which extends active learning from binary classification to multi-class classification using a unified representation with margin-based loss functions. The experimental results on the TREC03 semantic feature extraction task shows that the proposed active learning approach works effectively even with a significantly reduced amount of labeled data.