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DOMAIN ADAPTATION FOR IMAGE CLASSIFICATION WITH CLASS PRIORS
DOMAIN ADAPTATION FOR IMAGE CLASSIFICATION WITH CLASS PRIORS
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机译：具有优先级的图像分类领域自适应
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摘要
In camera-based object labeling, boost classifier is trained to classify an image represented by feature vector x using a target domain training set DT of labeled feature vectors representing images acquired by the same camera and a plurality of source domain training sets DS1,...,DSN acquired by other cameras. The training applies an adaptive boosting (AdaBoost) algorithm to generate base classifiers hr(x) and weights βr. The rth iteration of the AdaBoost algorithm trains candidate base classifiers each trained on a training set DTUDSk, and selects hr(x) from previously trained candidate base classifiers. The target domain training set DT 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.
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