Joint audio-visual patterns often exist in videos and provide strong multi-modal cues for detecting multimedia events. However, conventional methods generally fuse the visual and audio information only at a superficial level, without adequately exploring deep intrinsic joint patterns. In this paper, we propose a joint audio-visual bi-modal representation, called bi-modal words. We first build a bipartite graph to model relation across the quantized words extracted from the visual and audio modalities. Partitioning over the bipartite graph is then applied to construct the bi-modal words that reveal the joint patterns across modalities. Finally, different pooling strategies are employed to re-quantize the visual and audio words into the bi-modal words and form bimodal Bag-of-Words representations that are fed to subsequent multimedia event classifiers. We experimentally show that the proposed multi-modal feature achieves statistically significant performance gains over methods using individual visual and audio features alone and alternative multi-modal fusion methods. Moreover, we found that average pooling is the most suitable strategy for bi-modal feature generation.
In video event detection, Ye et al. REF model the relation between audio and visual modalities within a bipartite graph followed by a partitioning over this graph to reveal the joint patterns.
Joint audio-visual bi-modal codewords for video event detection
ICMR '12
Computer Science