Domain adaptation for image classification with class priors

Abstract

In camera-based object labeling, boost classifier ƒ(x)=Σβh(x) is trained to classify an image represented by feature vector x using a target domain training set Dof labeled feature vectors representing images acquired by the same camera and a plurality of source domain training sets D, . . . , Dacquired by other cameras. The training applies an adaptive boosting (AdaBoost) algorithm to generate base classifiers h(x) and weights β. The riteration of the AdaBoost algorithm trains candidate base classifiers h(x) each trained on a training set D∪D, and selects h(x) from previously trained candidate base classifiers. The target domain training set Dmay be expanded based on a prior estimate of the labels distribution for the target domain. The object labeling system may be a vehicle identification system, a machine vision article inspection system, or so forth.