Active Learning in Multiple Modalities for Semantic Feature Extraction from Video

Active learning has been demonstrated to be a useful tool to reduce human labeling effort for many multimedia applications. However, most of the previous work on multimedia active learning has gloss the multi-modality problem very much. From several experimental results, multi-modality fusion plays an important role to boost performance of multimedia classification. In this paper, we present a multi-modality active learning approach which enhances the process of active learning approach from single-modality to multi-modality. The experimental results on the TRECVID 2004 semantic feature extraction task show that the proposed active learning approach works more effectively than single-modality approach and also demonstrate a significantly reduced amount of labeled data.