Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multi-modality web video categorization
Proceedings of the international workshop on Workshop on multimedia information retrieval
A Discriminative Kernel-Based Approach to Rank Images from Text Queries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genre-specific semantic video indexing
Proceedings of the ACM International Conference on Image and Video Retrieval
Content-based video genre classification using multiple cues
Proceedings of the 3rd international workshop on Automated information extraction in media production
ShotTagger: tag location for internet videos
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Automatic concept-to-query mapping for web-based concept detector training
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Cross-modal categorisation of user-generated video sequences
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Multimodal genre classification of TV programs and YouTube videos
Multimedia Tools and Applications
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While hierarchies are powerful tools for organizing content in other application areas, current web video platforms offer only limited support for a taxonomy-based browsing. To overcome this limitation, we present a framework called TubeFiler. Its two key features are an automatic multimodal categorization of videos into a genre hierarchy, and a support of additional fine-grained hierarchy levels based on unsupervised learning. We present experimental results on real-world YouTube clips with a 2-level 46-category genre hierarchy, indicating that - though the problem is clearly challenging - good category suggestions can be achieved. For example, if TubeFiler suggests 5 categories, it hits the right one (or at least its supercategory) in 91.8% of cases.