Cost-sensitive learning of hierarchical tree classifiers for large-scale image classification and novel category detection
Visual tree to organize large-scale object classes hierarchically and determine inter-related learning tasks automatically. Multi-task structural learning for joint classifier training to enhance their discrimination power significantly. Hierarchical learning to leverage inter-level constraints for classifier training and limiting inter-level error propagation. Task and tree parallelism to scale up our hierarchical learning algorithm for large-scale image classification. Cost-sensitive learning and incremental learning for training and detecting for new object classes more effectively.
hierarchical tree classifiers large-scale image classification novel category detection visual tree incremental learning visual forest cost-sensitive hierarchical learning