Elements of information theory
Elements of information theory
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Journal of Machine Learning Research
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Large scale manifold transduction
Proceedings of the 25th international conference on Machine learning
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
Proceedings of the 18th international conference on World wide web
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
IEEE Transactions on Information Theory
Automatic image tagging through information propagation in a query log based graph structure
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Towards multi-semantic image annotation with graph regularized exclusive group lasso
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Fast semantic image retrieval based on random forest
Proceedings of the 20th ACM international conference on Multimedia
Semantic context learning with large-scale weakly-labeled image set
Proceedings of the 21st ACM international conference on Information and knowledge management
Accelerated large scale optimization by concomitant hashing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Improving image tags by exploiting web search results
Multimedia Tools and Applications
A cross-media evolutionary timeline generation framework based on iterative recommendation
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Multimedia information retrieval on the social web
Proceedings of the 22nd international conference on World Wide Web companion
MLRank: Multi-correlation Learning to Rank for image annotation
Pattern Recognition
Robust image annotation via simultaneous feature and sample outlier pursuit
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Towards efficient sparse coding for scalable image annotation
Proceedings of the 21st ACM international conference on Multimedia
Web media semantic concept retrieval via tag removal and model fusion
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Computer Vision and Image Understanding
Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion
International Journal of Multimedia Data Engineering & Management
Large-scale multilabel propagation based on efficient sparse graph construction
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Annotating large-scale image corpus requires huge amount of human efforts and is thus generally unaffordable, which directly motivates recent development of semi-supervised or active annotation methods. In this paper we revisit this notoriously challenging problem and develop a novel multi-label propagation scheme, whereby both the efficacy and accuracy of large-scale image annotation are further enhanced. Our investigation starts from a survey of previous graph propagation based annotation approaches, wherein we analyze their main drawbacks when scaling up to large-scale datasets and handling multi-label setting. Our proposed scheme outperforms the state-of-the-art algorithms by making the following contributions. 1) Unlike previous approaches that propagate over individual label independently, our proposed large-scale multi-label propagation (LSMP) scheme encodes the tag information of an image as a unit label confidence vector, which naturally imposes inter-label constraints and manipulates labels interactively. It then utilizes the probabilistic Kullback-Leibler divergence for problem formulation on multi-label propagation. 2) We perform the multi-label propagation on the so-called hashing-based L1-graph, which is efficiently derived with Locality Sensitive Hashing approach followed by sparse L1-graph construction within the individual hashing buckets. 3) An efficient and convergency provable iterative procedure is presented for problem optimization. Extensive experiments on NUS-WIDE dataset (both lite version with 56k images and full version with 270k images) well validate the effectiveness and scalability of the proposed approach.