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This paper proposes a method for domain adaptation that extends the maximummargin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2distance constraints between samples of different domains; thus, our method isdenoted as MMDTL2. Motivated by the differences between the images taken bynarrow band imaging (NBI) endoscopic devices, we utilize different NBI devicesas different domains and estimate the transformations between samples ofdifferent domains, i.e., image samples taken by different NBI endoscopesystems. We first formulate the problem in the primal form, and then derive thedual form with much lesser computational costs as compared to the naiveapproach. From our experimental results using NBI image datasets from twodifferent NBI endoscopic devices, we find that MMDTL2 is better than MMDT andalso support vector machines without adaptation, especially when NBI imagefeatures are high-dimensional and the per-class training samples are greaterthan 20.

Domain Adaptation with L2 constraints for classifying images from different endoscope systems