Modality Mixture Projections for Semantic Video Event Detection

Event detection is one of the most fundamental components for various kinds of domain applications of video information system. In recent years,, it has gained a considerable interest of practitioners and academics from different areas. While detecting video event has been the subject of extensive research efforts recently. much less existing approach has considered multimodal information and related efficiency issues. In this paper, we use a subspace selection technique to achieve fast and accurate video event detection using a subspace selection technique. The approach is capable of discriminating different classes and preserving the intramodal geometry of samples within an identical class. With the method, feature vectors presenting different kind of multi data can be easily projected from different identities and modalities onto a unified subspace, on which recognition process can be performed. Furthermore, the training stage is carried out once and we have a unified transformation matrix to project different modalities. Unlike existing multimodal detection systems, the new system works well when some modalities are not available. Experimental results based on soccer video and TRECVID news video collections demonstrate the effectiveness, efficiency and robustness of the proposed NIMP for individual recognition tasks in comparison to the existing approaches.