Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reranking Methods for Visual Search
IEEE MultiMedia
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting image retrieval through aggregating search results based on visual annotations
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Learning tag relevance by neighbor voting for social image retrieval
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
Image categorization combining neighborhood methods and boosting
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
WSMC '09 Proceedings of the 1st workshop on Web-scale multimedia corpus
Tag refinement by regularized LDA
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Real-time bag of words, approximately
Proceedings of the ACM International Conference on Image and Video 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
Tag suggestion and localization in user-generated videos based on social knowledge
Proceedings of second ACM SIGMM workshop on Social media
Content-based tag processing for Internet social images
Multimedia Tools and Applications
Automatic concept-to-query mapping for web-based concept detector training
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Tag-based social image search with visual-text joint hypergraph learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Enriching and localizing semantic tags in internet videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Personalizing automated image annotation using cross-entropy
MM '11 Proceedings of the 19th ACM international conference on Multimedia
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A social network for video annotation and discovery based on semantic profiling
Proceedings of the 21st international conference companion on World Wide Web
Fusing concept detection and geo context for visual search
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
Multimedia Tools and Applications
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
Hi-index | 0.00 |
Interpreting the relevance of a user-contributed tag with respect to the visual content of an image is an emerging problem in social image retrieval. In the literature this problem is tackled by analyzing the correlation between tags and images represented by specific visual features. Unfortunately, no single feature represents the visual content completely, e.g., global features are suitable for capturing the gist of scenes, while local features are better for depicting objects. To solve the problem of learning tag relevance given multiple features, we introduce in this paper two simple and effective methods: one is based on the classical Borda Count and the other is a method we name UniformTagger. Both methods combine the output of many tag relevance learners driven by diverse features in an unsupervised, rather than supervised, manner. Experiments on 3.5 million social-tagged images and two test sets verify our proposal. Using learned tag relevance as updated tag frequency for social image retrieval, both Borda Count and UniformTagger outperform retrieval without tag relevance learning and retrieval with single-feature tag relevance learning. Moreover, the two unsupervised methods are comparable to a state-of-the-art supervised alternative, but without the need of any training data.