Domain Adaptation for Visual Recognition

This monograph provides a comprehensive overview of domain adaptation solutions for visual recognition problems. It discusses the existing adaptation techniques in the literature, which are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations. It also analyzes the challenges posed by the realm of "big visual data" in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability