Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Boosting Image Orientation Detection with Indoor vs. Outdoor Classification
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Self-Training of Object Detection Models
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
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
Hidden Markov models for automatic annotation and content-based retrieval of images and video
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Semi-automatic video annotation based on active learning with multiple complementary predictors
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Information Theory - Part 2
Video annotation by graph-based learning with neighborhood similarity
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
Optimizing training set construction for video semantic classification
EURASIP Journal on Advances in Signal Processing
Study on the combination of video concept detectors
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Transductive multi-label learning for video concept detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Semi-supervised kernel density estimation for video annotation
Computer Vision and Image Understanding
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Relevance filtering meets active learning: improving web-based concept detectors
Proceedings of the international conference on Multimedia information retrieval
Kernel-based linear neighborhood propagation for semantic video annotation
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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
MQSS: multimodal query suggestion and searching for video search
Multimedia Tools and Applications
Social image annotation via cross-domain subspace learning
Multimedia Tools and Applications
Image annotation by semi-supervised cross-domain learning with group sparsity
Journal of Visual Communication and Image Representation
A spatio-temporal pyramid matching for video retrieval
Computer Vision and Image Understanding
Multimedia encyclopedia construction by mining web knowledge
Signal Processing
Hi-index | 0.00 |
Insufficiency of labeled training data is a major obstacle for automatically annotating large-scale video databases with semantic concepts. Existing semi-supervised learning algorithms based on parametric models try to tackle this issue by incorporating the information in a large amount of unlabeled data. However, they are based on a "model assumption" that the assumed generative model is correct, which usually cannot be satisfied in automatic video annotation due to the large variations of video semantic concepts. In this paper, we propose a novel semi-supervised learning algorithm, named Semi Supervised Learning by Kernel Density Estimation (SSLKDE), which is based on a non-parametric method, and therefore the "model assumption" is avoided. While only labeled data are utilized in the classical Kernel Density Estimation (KDE) approach, in SSLKDE both labeled and unlabeled data are leveraged to estimate class conditional probability densities based on an extended form of KDE. We also investigate the connection between SSLKDE and existing graph-based semi-supervised learning algorithms. Experiments prove that SSLKDE significantly outperforms existing supervised methods for video annotation.