Abstract

Image tagging is a growing application on social media web-sites, however, the performance of many auto-tagging meth-ods are often poor. Recent work has exploited an image’s context (e.g. time and location) in the tag recommendation process, where tags which co-occur highly within a given time interval or geographical area are promoted. These models, however, fail to address how and when different im-age contexts can be combined. In this paper, we propose a weighted tag recommendation model, building on an exist-ing state-of-the-art, which varies the importance of time and location in the recommendation process, based on a given set of input tags. By retrieving more temporally and geo-graphically relevant tags, we achieve statistically significant improvements to recommendation accuracy when testing on 519k images collected from Flickr. The result of this paper is an important step towards more effective image annotation and retrieval systems.