Tagging Personal Photos with Transfer Deep Learning

The advent of mobile devices and media cloud services has led to the unprecedented growing of personal photo collections. One of the fundamental problems in managing the increasing number of photos is automatic image tagging. Existing research has pre-dominantly focused on tagging general Web images with a well-labelled image database, e.g., ImageNet. However, they can only achieve limited success on personal photos due to the domain gap-s between personal photos and Web images. These gaps originate from the differences in semantic distribution and visual appearance. To deal with these challenges, in this paper, we present a novel transfer deep learning approach to tag personal photos. Specifi-cally, to solve the semantic distribution gap, we have designed an ontology consisting of a hierarchical vocabulary tailored for per-sonal photos. This ontology is mined from 10, 000 active users i