  Labelled image datasets have played a critical role in high-level image
understanding. However, the process of manual labelling is both time-consuming
and labor intensive. To reduce the cost of manual labelling, there has been
increased research interest in automatically constructing image datasets by
exploiting web images. Datasets constructed by existing methods tend to have a
weak domain adaptation ability, which is known as the "dataset bias problem".
To address this issue, we present a novel image dataset construction framework
that can be generalized well to unseen target domains. Specifically, the given
queries are first expanded by searching the Google Books Ngrams Corpus to
obtain a rich semantic description, from which the visually non-salient and
less relevant expansions are filtered out. By treating each selected expansion
as a "bag" and the retrieved images as "instances", image selection can be
formulated as a multi-instance learning problem with constrained positive bags.
We propose to solve the employed problems by the cutting-plane and
concave-convex procedure (CCCP) algorithm. By using this approach, images from
different distributions can be kept while noisy images are filtered out. To
verify the effectiveness of our proposed approach, we build an image dataset
with 20 categories. Extensive experiments on image classification,
cross-dataset generalization, diversity comparison and object detection
demonstrate the domain robustness of our dataset.
