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 both textual and visual clues. Experiments on 1.5 million Flickr photos and 10 000 Corel images verify the proposed method.

DOI: 10.1109/ICASSP.2009.4960434

Extracted Key Phrases

5 Figures and Tables

Cite this paper

@article{Li2009AnnotatingIB, title={Annotating images by harnessing worldwide user-tagged photos}, author={Xirong Li and Cees Snoek and Marcel Worring}, journal={2009 IEEE International Conference on Acoustics, Speech and Signal Processing}, year={2009}, pages={3717-3720} }