Towards Blur Mixed Instance Search Combined with Image-wise and Object-wise Feature

Abstract

With development of intelligent robots and autonomous driving, images/videos with motion blurs occur frequently. However, these images/videos with blurs might be retrieved. Instance search has been studied for many years in the computer vision field. However, few works are focused on instance search on blurred images. In this paper, we propose a framework of instance search on blurred image datasets which is built on three components including query blur mixing, image-wise feature based ranking, and instance-wise feature based re-ranking. Due to lack of the benchmark, we also collect and build a blurred image dataset on which we conduct performance experiments. Experimental results show that our solution is significantly promising in instance search task.