Dynamic visual category learning

Tom Yeh, Trevor Darrell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

28 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
DOIs
StatePublished - 2008
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR - Anchorage, AK, United States
Duration: 23 Jun 200828 Jun 2008

Publication series

Name26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Conference

Conference26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Country/TerritoryUnited States
CityAnchorage, AK
Period23/06/0828/06/08

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