Dynamic visual category learning

TitleDynamic visual category learning
Publication TypeConference Papers
Year of Publication2008
AuthorsTom Yeh, Darrell T
Conference NameComputer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Date Published2008/06//
ISBN Number978-1-4244-2242-5

Dynamic visual category learning calls for efficient adaptation as new training images become available or new categories are defined, existing training images or categories become modified or obsolete, or when categories are divided into subcategories or merged together. We develop novel methods for efficient incremental learning of SVM-based visual category classifiers to handle such dynamic tasks. Our method exploits previous classifier estimates to more efficiently learn the optimal parameters for the current set of training images and categories. We show empirically that for dynamic visual category tasks, our incremental learning methods are significantly faster than batch retraining.