Mining multi-tag association for image tagging
World Wide Web
Contextual Video Recommendation by Multimodal Relevance and User Feedback
ACM Transactions on Information Systems (TOIS)
Multimedia Tools and Applications
Mining tweets for tag recommendation on social media
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Discovering small-world in association link networks for web-based learning
MTDL '11 Proceedings of the third international ACM workshop on Multimedia technologies for distance learning
Context sensitive tag expansion with information inference
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Classifier-specific intermediate representation for multimedia tasks
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Local image tagging via graph regularized joint group sparsity
Pattern Recognition
Tagging photos using users' vocabularies
Neurocomputing
Near-lossless semantic video summarization and its applications to video analysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Annotation for free: video tagging by mining user search behavior
Proceedings of the 21st ACM international conference on Multimedia
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Unlimited vocabulary annotation of multimedia documents remains elusive despite progress solving the problem in the case of a small, fixed lexicon. Taking advantage of the repetitive nature of modern information and online media databases with independent annotation instances, we present an approach to automatically annotate multimedia documents that uses mining techniques to discover new annotations from similar documents and to filter existing incorrect annotations. The annotation set is not limited to words that have training data or for which models have been created. It is limited only by the words in the collective annotation vocabulary of all the database documents. A graph reinforcement method driven by a particular modality (e.g., visual) is used to determine the contribution of a similar document to the annotation target. The graph supplies possible annotations of a different modality (e.g., text) that can be mined for annotations of the target. Experiments are performed using videos crawled from YouTube. A customized precision-recall metric shows that the annotations obtained using the proposed method are superior to those originally existing for the document. These extended, filtered tags are also superior to a state-of-the-art semi-supervised technique for graph reinforcement learning on the initial user-supplied annotations.