MM '08 Proceedings of the 16th ACM international conference on Multimedia
Graph-Based Pairwise Learning to Rank for Video Search
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Correlative linear neighborhood propagation for video annotation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Similarity search on Bregman divergence: towards non-metric indexing
Proceedings of the VLDB Endowment
Collection-based sparse label propagation and its application on social group suggestion from photos
ACM Transactions on Intelligent Systems and Technology (TIST)
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
ACM Transactions on Intelligent Systems and Technology (TIST)
Mining multi-tag association for image tagging
World Wide Web
Attribute learning for understanding unstructured social activity
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Video search and indexing with reinforcement agent for interactive multimedia services
ACM Transactions on Embedded Computing Systems (TECS) - Special issue on embedded systems for interactive multimedia services (ES-IMS)
Local image tagging via graph regularized joint group sparsity
Pattern Recognition
Multiple kernel local Fisher discriminant analysis for face recognition
Signal Processing
Effective transfer tagging from image to video
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Video event description in scene context
Neurocomputing
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The insufficiency of labeled training data for representing the distribution of the entire dataset is a major obstacle in automatic semantic annotation of large-scale video database. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, are promising to solve this problem. In this paper, a novel graph-based semi-supervised learning method named kernel linear neighborhood propagation (KLNP) is proposed and applied to video annotation. This approach combines the consistency assumption, which is the basic assumption in semi-supervised learning, and the local linear embedding (LLE) method in a nonlinear kernel-mapped space. KLNP improves a recently proposed method linear neighborhood propagation (LNP) by tackling the limitation of its local linear assumption on the distribution of semantics. Experiments conducted on the TRECVID data set demonstrate that this approach outperforms other popular graph-based semi-supervised learning methods for video semantic annotation.