Towards data-driven estimation of image tag relevance using visually similar and dissimilar folksonomy images

Abstract

Given that the presence of non-relevant tags in an image folksonomy hampers the effective organization and retrieval of images, this paper discusses a novel technique for estimating the relevance of user-supplied tags with respect to the content of a seed image. Specifically, this paper proposes to compute the relevance of image tags by making use of both visually similar and dissimilar images. That way, compared to tag relevance estimation only using visually similar images, the difference in tag relevance between tags relevant and tags irrelevant with respect to the content of a seed image can be increased at a limited increase in computational cost, thus making it more straightforward to distinguish between them. The latter is confirmed through experimentation with subsets of MIRFLICKR-25000 and MIRFLICKR-1M, showing that tag relevance estimation using both visually similar and dissimilar images allows achieving more effective image tag refinement and tag-based image retrieval than tag relevance estimation only using visually similar images.