Representations of quasi-Newton matrices and their use in limited memory methods
Mathematical Programming: Series A and B
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multimedia semantic indexing using model vectors
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
International Journal of Computer Vision
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refining video annotation by exploiting pairwise concurrent relation
Proceedings of the 15th international conference on Multimedia
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
Correlative multilabel video annotation with temporal kernels
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Image annotation via graph learning
Pattern Recognition
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Context-based multi-label image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Factor graph framework for semantic video indexing
IEEE Transactions on Circuits and Systems for Video Technology
Automatic image annotation using tag-related random search over visual neighbors
Proceedings of the 21st ACM international conference on Information and knowledge management
An interactive semi-supervised approach for automatic image annotation
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
A heterogenous automatic feedback semi-supervised method for image reranking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Multi-label image/video annotation is a challenging task that allows to correlate more than one high-level semantic keyword with an image/video-clip. Previously, a single model is usually used for the annotation task, with relatively large variance in performance. The correlation among the annotation keywords should also be considered. In this paper, to reduce the performance variance and exploit the correlation between keywords, we propose the En-CRF (Ensemble based on Conditional Random Field) method. In this method, multiple models are first trained for each keyword, then the predictions of these models and the correlations between keywords are incorporated into a conditional random field. Experimental results on benchmark data set, including Corel5k and TRECVID 2005, show that the En-CRF method is superior or highly competitive to several state-of-the-art methods.