Graph based multimodal word clustering for video event detection

Combining diverse low-level features from multiple modalities has consistently improved performance over a range of video processing tasks, including event detection. In our work, we study graph based clustering techniques for integrating information from multiple modalities by identifying word clusters spread across the different modalities. We present different methods to identify word clusters including word similarity graph partitioning, word-video co-clustering and Latent Semantic Indexing and the impact of different metrics to quantify the co-occurrence of words. We present experimental results on a ≈45000 video dataset used in the TRECVID MED 11 evaluations. Our experiments show that multimodal features have consistent performance gains over the use of individual features. Further, word similarity graph construction using a complete graph representation consistently improves over partite graphs and early fusion based multimodal systems. Finally, we see additional performance gains by fusing multimodal features with individual features.