Multimedia genre characterisation with fuzzy embedding classifiers
Proceedings of the 2008 Ambi-Sys workshop on Ambient media delivery and interactive television
TV Genre Classification Using Multimodal Information and Multilayer Perceptrons
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Parallel neural networks for multimodal video genre classification
Multimedia Tools and Applications
Discriminative optical flow tensor for video semantic analysis
Computer Vision and Image Understanding
Characterizing Multimedia Objects through Multimodal Content Analysis and Fuzzy Fingerprints
Advanced Internet Based Systems and Applications
Text-based video content classification for online video-sharing sites
Journal of the American Society for Information Science and Technology
Genre-specific semantic video indexing
Proceedings of the ACM International Conference on Image and Video Retrieval
Automatic video genre categorization and event detection techniques on large-scale sports data
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
Modeling nuisance variabilities with factor analysis for GMM-based audio pattern classification
Computer Speech and Language
Multimedia Tools and Applications
Content-based intelligent video recorder with its implementation on sports video
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
A rough set approach to video genre classification
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
A Generic Approach for Systematic Analysis of Sports Videos
ACM Transactions on Intelligent Systems and Technology (TIST)
Video genre classification using weighted kernel logistic regression
Advances in Multimedia - Special issue on Multimedia Applications for Smart Device in Ubiquitous Environments
| Hi-index | 0.00 |
We investigate the problem of automated video classification by analysing the low-level audio-visual signal patterns along the time course in a holistic manner. Five popular TV broadcast genre are studied including sports, cartoon, news, commercial and music. A novel statistically based approach is proposed comprising two important ingredients designed for implicit semantic content characterisation and class identities modelling. First, a spatial-temporal audio-visual "concatenated" feature vector is composed, aiming to capture crucial clip-level video structure information inherent in a video genre. Second, the feature vector is further processed using principal component analysis to reduce the spatial-temporal redundancy while exploiting the correlations between feature elements. This gives rise to a compact representation fro effective probabilistic modelling of each video genre. Extensive experiments are conducted assessing various aspects of the approach and their influence on the overall system performance.