Tag-based social image search with visual-text joint hypergraph learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Tag suggestion and localization for web videos by bipartite graph matching
WSM '11 Proceedings of the 3rd ACM SIGMM international workshop on Social media
An automatic web-oriented multimedia extraction and multiresolution visualization scheme
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
Discriminative factor alignment across heterogeneous feature space
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Multimedia Tools and Applications
Using contextual spaces for image re-ranking and rank aggregation
Multimedia Tools and Applications
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Photo sharing websites such as Flickr host a massive amount of social images with user-provided tags. However, these tags are often imprecise and incomplete, which essentially limits tag-based image indexing and related applications. To tackle this issue, we propose an image retagging scheme that aims at refining the quality of the tags. The retagging process is formulated as a multiple graph-based multi-label learning problem, which simultaneously explores the visual content of the images, semantic correlation of the tags as well as the prior information provided by users. Different from classical single graph-based multi-label learning algorithms, the proposed algorithm propagates the information of each tag along an individual tag-specific similarity graph, which reflects the particular relationship among the images with respect to the specific tag and at the same time the propagations of different tags interact with each other in a collaborative way with an extra tag similarity graph. In particular, we present a robust tag-specific visual sub-vocabulary learning algorithm for the construction of those tag-specific graphs. Experimental results on two benchmark Flickr image datasets demonstrate the effectiveness of our proposed image retagging scheme. We also show the remarkable performance improvements brought by retagging in the task of image ranking.