Domain adaptive boosting method and its applications

Jie Geng; Zhenjiang Miao

doi:10.1117/1.JEI.24.2.023038

28 April 2015 Domain adaptive boosting method and its applications

Jie Geng, Zhenjiang Miao

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 2, 023038 (April 2015). https://doi.org/10.1117/1.JEI.24.2.023038

Abstract

Differences of data distributions widely exist among datasets, i.e., domains. For many pattern recognition, nature language processing, and content-based analysis systems, a decrease in performance caused by the domain differences between the training and testing datasets is still a notable problem. We propose a domain adaptation method called domain adaptive boosting (DAB). It is based on the AdaBoost approach with extensions to cover the domain differences between the source and target domains. Two main stages are contained in this approach: source-domain clustering and source-domain sample selection. By iteratively adding the selected training samples from the source domain, the discrimination model is able to achieve better domain adaptation performance based on a small validation set. The DAB algorithm is suitable for the domains with large scale samples and easy to extend for multisource adaptation. We implement this method on three computer vision systems: the skin detection model in single images, the video concept detection model, and the object classification model. In the experiments, we compare the performances of several commonly used methods and the proposed DAB. Under most situations, the DAB is superior.

Citation Download Citation

Jie Geng and Zhenjiang Miao "Domain adaptive boosting method and its applications," Journal of Electronic Imaging 24(2), 023038 (28 April 2015). https://doi.org/10.1117/1.JEI.24.2.023038

Published: 28 April 2015

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