A framework for classifier adaptation and its applications in concept detection

There is often a need to adapt supervised classifiers such as semantic concept detectors across different domains of data. This paper describes a generic framework for function-level classifier adaptation based on regularized loss minimization. It directly modifies the decision function of an existing classifier of any type into a classifier for a new domain, based on limited labeled data in the new domain and no "old data", which makes it an efficient and flexible framework. We then extend this framework to adapt multiple classifiers into one classifier, with the weights of existing classifiers learned automatically to reflect their utility. We elaborate on two concrete adaptation algorithms derived from the framework, namely adaptive SVM and multi-adaptive SVM, for one-to-one and many-to-one adaptation respectively. In the experiments of adapting semantic concept detectors across video channels/types, our adaptation approach is proven to be superior to using original (unadapted) classifiers or building new ones in terms of accuracy and labeling effort.