Image tag re-ranking by coupled probability transition

Abstract

The large amount of user-tagged images on social networks is helpful to facilitate image management and image search. However, many tags are weakly relevant or irrelevant to the visual content, resulting in unsatisfactory performance in tag related applications. In this paper, we propose a coupled probability transition algorithm to estimate the text-visual group relevance from the observed data and then leverage it to predict tag relevance for a new query image. The visual group for a given tag is a cluster of images that are visually similar and share the same tag. The tag-visual group relevance is uncovered by exploiting the mutual reinforcement in visual space and semantic space alternatively. Experiments on NUS-WIDE dataset show the validity and superiority of the proposed approach over existing methods.