Automatic partitioning of full-motion video
Multimedia Systems
The nature of statistical learning theory
The nature of statistical learning theory
Automatic recognition of film genres
Proceedings of the third ACM international conference on Multimedia
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Rule-based video classification system for basketball video indexing
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Data Mining and Knowledge Discovery
Visually Searching the Web for Content
IEEE MultiMedia
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Video Scene Segmentation via Continuous Video Coherence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A mid-level representation framework for semantic sports video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Confidence-based dynamic ensemble for image annotation and semantics discovery
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Video retrieval using spatio-temporal descriptors
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Probabilistic Space-Time Video Modeling via Piecewise GMM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure analysis of soccer video with domain knowledge and hidden Markov models
Pattern Recognition Letters - Video computing
Supervised classification for video shot segmentation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Enhanced access to digital video through visually rich interfaces
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A probabilistic framework for semantic video indexing, filtering,and retrieval
IEEE Transactions on Multimedia
Toward automatic extraction of expressive elements from motion pictures: tempo
IEEE Transactions on Multimedia
Computable scenes and structures in films
IEEE Transactions on Multimedia
Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing
IEEE Transactions on Image Processing
Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework
IEEE Transactions on Circuits and Systems for Video Technology
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Using object and trajectory analysis to facilitate indexing and retrieval of video
Knowledge-Based Systems
Video genre classification using weighted kernel logistic regression
Advances in Multimedia - Special issue on Multimedia Applications for Smart Device in Ubiquitous Environments
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As large collections of videos become one key component of digital libraries, there is an urgent need of semantic video classification and feature subset selection to enable more effective video database organization and retrieval. However, most existing techniques for classifier training require a large number of labeled samples to learn correctly and suffer from the problems of context and concept uncertainty when only a limited number of labeled samples are available. To address the problems of context and concept uncertainty, we have proposed a novel framework to achieve incremental classifier training by integrating a limited number of labeled samples with a large number of unlabeled samples. Specifically, the contributions of this paper include: (a) Using the salient objects to achieve a middle-level understanding of video contents and enhance the quality of features on discriminating among different semantic video concepts; (b) Modeling the semantic video concepts by using the finite mixture models to approximate the class distributions of the relevant salient objects; (c) Developing an adaptive EM algorithm to integratethe unlabeled samples to enable incremental classifier training and address the problem of context uncertainty; (d) Proposing a cost-sensitive video classification technique to address the problem of concept uncertainty over time; (e) Supporting automatic video annotation via semantic classification Our experimental results in a certain domain of medical education videos have also been provided a convincing proof of our conclusions.