Learning to rank tags

Abstract

Social images sharing websites, such as Flickr and Picasa, are becoming very popular nowadays. Users are generally recommended to annotate images with free tags, yet these tags are orderless, and thus quite limited for applications like image search, retrieval and management. In this paper, we present a novel semi-supervised learning framework to rank image tags, which learns a ranking projection with theoretic guarantee from visual words distribution to the relevant tags distribution, and then uses it for ranking new image tags. Also as the manual ranking is laborious especially for large scale data collections, we propose an active learning scheme to guide the user ranking process and efficiently obtain the informative tag ranking information. This scheme improves the overall ranking result significantly with few user feedbacks. Experiments on both image benchmark and real Flickr photo collection show the practicability and efficiency of our proposed framework, which also further improves the performance of ranked tag recommendation application. Copyright © 2010 ACM.