Exploring tag relevance for image tag re-ranking
Jie Xiao, Wengang Zhou, Qi Chuan Tian · 2012
In this paper, we propose to explore the relevance between tags for image tag re-ranking. The key component is to define a global tag-tag similarity matrix, which is achieved by analysis in both semantic and visual aspects. The text semantic relevance is explored by the Latent Semantic Indexing (LSI) model [1].For the visual information, the tag-relevance can be propagated by reconstructing exemplar images with visually and semantically consistent images. Based on our tag relevance matrix, a random-walk approach is leveraged to discover the significance of each tag. Finally, all tags in an image are re-ranked by their significance values. Extensive experiments show its effectiveness on an image dataset with a large tags vocabulary.