Semantic video classification by integrating flexible mixture model with adaptive EM algorithm
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Structure analysis of soccer video with domain knowledge and hidden Markov models
Pattern Recognition Letters - Video computing
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
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Multi-layer multi-instance kernel for video concept detection
Proceedings of the 15th international conference on Multimedia
Structure-sensitive manifold ranking for video concept detection
Proceedings of the 15th international conference on Multimedia
Correlative multilabel video annotation with temporal kernels
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Semi-supervised kernel density estimation for video annotation
Computer Vision and Image Understanding
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
Instance-level semisupervised multiple instance learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Expert Systems with Applications: An International Journal
A New SVM Approach to Multi-instance Multi-label Learning
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Multi-instance multi-label learning
Artificial Intelligence
Ensemble multi-instance multi-label learning approach for video annotation task
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multi-Layer Multi-Instance Learning for Video Concept Detection
IEEE Transactions on Multimedia
Multi-instance multi-label learning with weak label
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Traditional approaches for automatic video annotation usually represent one video clip with a flat feature vector, neglecting the fact that video data contain natural structures. It is also noteworthy that a video clip is often relevant to multiple concepts. Indeed, the video annotation task is inherently a Multi-Instance Multi-Label learning (MIML) problem. Considering that manually annotating videos is labor-intensive and time-consuming, this paper proposes a semi-supervised MIML approach, SSMIML, which is able to exploit abundant unannotated videos to help improve the annotation performance. This approach takes label correlations into account, and enforces similar instances to share similar multi-labels. Evaluation on TREVID 2005 show that the proposed approach outperforms several state-of-the-art methods.