Incorporating concept ontology for hierarchical video classification, annotation, and visualization
Most existing content-based video retrieval (CBVR) systems are now amenable to support automatic low-level feature extraction, but they still have limited effectiveness from a user's perspective because of the semantic gap. Automatic video concept detection via semantic classification is one promising solution to bridge the semantic gap. To speed up SVM video classifier training in high-dimensional heterogeneous feature space, a novel multimodal boosting algorithm is proposed by incorporating feature hierarchy and boosting to reduce both the training cost and the size of training samples significantly. To avoid the inter-level error transmission problem, a novel hierarchical boosting scheme is proposed by incorporating concept ontology and multitask learning to boost hierarchical video classifier training through exploiting the strong correlations between the video concepts. To bridge the semantic gap between the available video concepts and the users' real needs, a novel hyperbolic visualization framework is seamlessly incorporated to enable intuitive query specification and evaluation by acquainting the users with a good global view of large-scale video collections. Our experiments in one specific domain of surgery education videos have also provided very convincing results.
concept ontology video classification annotation semantic gap multimodal boosting hierarchical boosting multitask learning hyperbolic visualization