Domain adaptation for image classification with class priors

摘要

In camera-based object labeling, boost classifier ƒ^T(x)=Σ_r=1^Mβ_rh_r(x) is trained to classify an image represented by feature vector x using a target domain training set D_T of labeled feature vectors representing images acquired by the same camera and a plurality of source domain training sets D_S1, . . . , D_SN acquired by other cameras. The training applies an adaptive boosting (AdaBoost) algorithm to generate base classifiers h_r(x) and weights β_r. The r^th iteration of the AdaBoost algorithm trains candidate base classifiers h_r^k(x) each trained on a training set D_T∪D_Sk, and selects h_r(x) from previously trained candidate base classifiers. The target domain training set D_T may 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.