One person labels one million images

Targeting the same objective of alleviating the manual work as automatic annotation, in this paper, we propose a novel framework with minimal human effort to manually annotate a large-scale image corpus. In this framework, a dynamic multi-scale cluster labeling strategy is proposed to manually label the clusters of similar image regions. The users label the multi-scale clusters of regions instead of individual images, thus each labeling operation can annotate hundreds or even thousands of images simultaneously with much reduced manual work. Meanwhile the manual labeling guarantees the accuracy of the labels. Compared to automatic annotation, the proposed framework is more flexible, general and effective, especially for annotating those labels with large semantic gaps. Experiments on NUS-WIDE dataset demonstrate that the proposed fast manual annotation framework is much more effective than automatic annotation and comparatively efficient.