Assembling Heterogeneous Domain Adaptation Methods for Image Classification

In this paper we report the contribution of XRCE team to the Domain Adaptation Challenge [10] organized in the framework of ImageCLEF 2014 competition [9]. We describe our approach to build an image classification system when a weak image annotation in the target domain is compensated by massively annotated images in source domains. One method is based using several heterogeneous methods for the domain adaptation aimed at the late fusion of the individual predictions. One big class of domain adaptation methods addresses a selective reuse of instances from source domains for target domain. We adopt from this class the adaptive boosting for weighting source instances which learns a combination of weak classifiers in the target domain. Another class of methods aims to transform both target and source domains in a common space. In this class we focused on metric learning approaches aimed at reducing distances between images from the same class and to increase distances of different classes independently if they are from source or target domain. Combined the above approaches with a ”brute-force” SVM-based approach we obtain a set of heterogeneous classifiers for class prediction of target instances. In order to improve the overall accuracy, we combine individual classifiers through different versions of majority voting. We describe different series of experiments including those submitted for the official competition and analyze their results.