Annotating images by harnessing worldwide user-tagged photos

Automatic image tagging is important yet challenging due to the semantic gap and the lack of learning examples to model a tag’s visual diversity. Meanwhile, social user tagging is creating rich multimedia content on the web. In this paper, we propose to combine the two tagging approaches in a search-based framework. For an unlabeled image, we first retrieve its visual neighbors from a large user-tagged image database. We then select relevant tags from the result images to annotate the unlabeled image. To tackle the unreliability and sparsity of user tagging, we introduce a joint-modality tag relevance estimation method which efficiently addresses

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Xirong Li, Cees G. M. Snoek, Marcel Worring
• Keywords: ICASSP 2009, Signal Processing, Tag Relevance Estimation, Tag’s Visual Diversity, Unlabeled Image