Automatic organization for digital photographs with geographic coordinates
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Visual language modeling for image classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
Automatic image tagging as a random walk with priors on the canonical correlation subspace
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Proceedings of the 18th international conference on World wide web
Proceedings of the 18th international conference on World wide web
Automatic video tagging using content redundancy
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
Leveraging loosely-tagged images and inter-object correlations for tag recommendation
Proceedings of the international conference on Multimedia
Multi-label boosting for image annotation by structural grouping sparsity
Proceedings of the international conference on Multimedia
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Image tagging is a task that automatically assigns the query image with semantic keywords called tags. Since tags and image visual content are represented in different feature space, how to merge the multiple features by their correlation to tag the query image is an important problem. However, most of existing approaches merge the features by using a relatively simple mechanism rather than fully exploiting the correlations between different features. In this paper, we propose a new approach to fusing different features and their correlation simultaneously for image tagging. Specifically, we employ a Feature Correlation Graph to capture the correlations between different features in an integrated manner, which take features as nodes and their correlations as edges. Then, a revised probabilistic model based on Markov Random Field is used to describe the graph for evaluating tag's relevance to query image. Experiments on large real-life corpuses collected from Flickr indicate the superiority of our proposed approach.