Many people search for foreground objects to use when editing images. While existing methods can retrieve candidates to aid in this, they are constrained to returning objects that belong to a pre-specified semantic class. We instead propose a novel problem of unconstrained foreground object (UFO) search and introduce a solution that supports efficient search by encoding the background image in the same latent space as the candidate foreground objects. A key contribution of our work is a cost-free, scalable approach for creating a large-scale training dataset with a variety of foreground objects of differing semantic categories per image location. Quantitative and human-perception experiments with two diverse datasets demonstrate the advantage of our UFO search solution over related baselines.
@article{arxiv.1908.03675,
title = {Unconstrained Foreground Object Search},
author = {Yinan Zhao and Brian Price and Scott Cohen and Danna Gurari},
journal= {arXiv preprint arXiv:1908.03675},
year = {2019}
}