Cost-sensitive learning of hierarchical tree classifiers for large-scale image classification and novel category detection
作者：
Highlights：
• 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.
摘要
Highlights•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.
论文关键词：Large-scale image classification,Novel category detection,Visual tree,Visual forest,Hierarchical tree classifiers,Cost-sensitive hierarchical learning,Incremental learning
论文评审过程：Received 21 May 2014, Revised 25 September 2014, Accepted 19 October 2014, Available online 31 October 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.10.025
