Cross-domain object recognition by output kernel learning

It is of great importance to investigate the domain adaptation problem as vision data is now available from a variety of sources. For adapting a classifier, the first problem is how to choose `source´ domain. The key issue here is measuring domain similarity. In this paper, we present one of the first studies on `domain similarity´ measure in the context of object recognition. We introduce an output kernel divergence as a similarity measure between different data domains, and propose using it as a criterion for domain selection for better recognition accuracy. We also propose a novel domain adaptation method using a vector-valued function with learned output kernels. Fundamentally different from existing work, we focus on the shift in the output kernel space, instead of handling the distribution shift in the input feature space. In addition, those previous methods could also be applied together with ours to improve the performance further. We demonstrate the ability of the proposed model to select and adapt between different domains, and report the state-of-art results on a benchmark data set.