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Random Forest (RF) is a successful paradigm for learning classifiers due toits ability to learn from large feature spaces and seamlessly integratemulti-class classification, as well as the achieved accuracy and processingefficiency. However, as many other classifiers, RF requires domain adaptation(DA) provided that there is a mismatch between the training (source) andtesting (target) domains which provokes classification degradation.Consequently, different RF-DA methods have been proposed, which not onlyrequire target-domain samples but revisiting the source-domain ones, too. Asnovelty, we propose three inherently different methods (Node-Adapt, Path-Adaptand Tree-Adapt) that only require the learned source-domain RF and a relativelyfew target-domain samples for DA, i.e. source-domain samples do not need to beavailable. To assess the performance of our proposals we focus on image-basedobject detection, using the pedestrian detection problem as challengingproof-of-concept. Moreover, we use the RF with expert nodes because it is acompetitive patch-based pedestrian model. We test our Node-, Path- andTree-Adapt methods in standard benchmarks, showing that DA is largely achieved.

Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest