Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Improved boosting algorithms using confidence-rated predictions
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Classification by pairwise coupling
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A Methodology for Mapping Scores to Probabilities
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Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
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Intelligent Indexing and Semantic Retrieval of Multimodal Documents
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
Improving Image Retrieval with Semantic Classification Using Relevance Feedback
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Context and configuration-based scene classification
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SVMTorch: support vector machines for large-scale regression problems
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ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
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CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Semantic video classification and feature subset selection under context and concept uncertainty
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Semantic video classification by integrating unlabeled samples for classifier training
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Multi-level annotation of natural scenes using dominant image components and semantic concepts
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Efficient propagation for face annotation in family albums
Proceedings of the 12th annual ACM international conference on Multimedia
Enhanced Perceptual Distance Functions and Indexing for Image Replica Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantics and feature discovery via confidence-based ensemble
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Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
SEVA: sensor-enhanced video annotation
Proceedings of the 13th annual ACM international conference on Multimedia
Semantic image classification with hierarchical feature subset selection
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A mutual semantic endorsement approach to image retrieval and context provision
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
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Large-scale video retrieval via semantic classification
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Modeling personal and social network context for event annotation in images
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An empirical study of inter-concept similarities in multimedia ontologies
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Contextual wisdom: social relations and correlations for multimedia event annotation
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PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Information Processing and Management: an International Journal
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Multimedia Tools and Applications
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MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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Providing accurate and scalable solutions to map low-level perceptual features to high-level semantics is critical for multimedia information organization and retrieval. In this paper, we propose a confidence-based dynamic ensemble (CDE) to overcome the shortcomings of the traditional static classifiers. In contrast to the traditional models, CDE can make dynamic adjustments to accommodate new semantics, to assist the discovery of useful low-level features, and to improve class-prediction accuracy. We depict two key components of CDE: a multi-level function that asserts class-prediction confidence, and the dynamic ensemble method based upon the confidence function. Through theoretical analysis and empirical study, we demonstrate that CDE is effective in annotating large-scale, real-world image datasets.