Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Semi-Supervised Cross Feature Learning for Semantic Concept Detection in Videos
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Semi-automatic video annotation based on active learning with multiple complementary predictors
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Semi-supervised learning for structured output variables
ICML '06 Proceedings of the 23rd international conference on Machine learning
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic video annotation by semi-supervised learning with kernel density estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Transductive support vector machines for structured variables
Proceedings of the 24th international conference on Machine learning
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Structure-sensitive manifold ranking for video concept detection
Proceedings of the 15th international conference on Multimedia
Optimizing multi-graph learning: towards a unified video annotation scheme
Proceedings of the 15th international conference on Multimedia
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Local-driven semi-supervised learning with multi-label
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Robust semantic concept detection in large video collections
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Improving video concept detection using spatio-temporal correlation
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
A transductive multi-label learning approach for video concept detection
Pattern Recognition
Semi-supervised genetic programming for classification
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Robust Video Content Analysis via Transductive Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
Bidirectional semi-supervised learning with graphs
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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Transductive video concept detection is an effective way to handle the lack of sufficient labeled videos. However, another issue, the multi-label interdependence, is not essentially addressed in the existing transductive methods. Most solutions only applied the transductive single-label approach to detect each individual concept separately, but ignoring the concept relation, or simply imposed the smoothness assumption over the multiple labels for each video, without indeed exploring the interdependence between the concepts. On the other hand, the semi-supervised extension of supervised multi-label classifiers, such as correlative multi-label support vector machines, is usually intractable and hence impractical due to the quite expensive computational cost. In this paper, we propose an effective transductive multi-label classification approach, which simultaneously models the labeling consistency between the visually similar videos and the multi-label interdependence for each video in an integrated framework. We compare the performance between the proposed approach and several representative transductive single-label and supervised multi-label classification approaches for the video concept detection task over the widely-used TRECVID data set. The comparative results demonstrate the superiority of the proposed approach.