The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Classification of summarized videos using hidden markov models on compressed chromaticity signatures
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
VRules: an effective association-based classifier for videos
Proceedings of the 2nd ACM international workshop on Multimedia databases
Joint categorization of queries and clips for web-based video search
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
How many high-level concepts will fill the semantic gap in news video retrieval?
Proceedings of the 6th ACM international conference on Image and video retrieval
Learning video preferences from video content
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
Exploring social tagging graph for web object classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations and Trends in Information Retrieval
Identification of extremist videos in online video sharing sites
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
A Multi-Pronged Approach to Improving Semantic Extraction of News Video
Journal of Signal Processing Systems
Learning automatic concept detectors from online video
Computer Vision and Image Understanding
Text-based video content classification for online video-sharing sites
Journal of the American Society for Information Science and Technology
Content-enriched classifier for web video classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A system for the semantic multimodal analysis of news audio-visual content
EURASIP Journal on Advances in Signal Processing
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
Improved video categorization from text metadata and user comments
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Audio-visual grouplet: temporal audio-visual interactions for general video concept classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Says who?: automatic text-based content analysis of television news
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
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Video classification is the first step toward multimedia content understanding. When video is classified into conceptual categories, it is usually desirable to combine evidence from multiple modalities. However, combination strategies in previous studies were usually ad hoc. We investigate a meta-classification combination strategy using Support Vector Machine, and compare it with probability-based strategies. Text features from closed-captions and visual features from images are combined to classify broadcast news video. The experimental results show that combining multimodal classifiers can significantly improve recall and precision, and our meta-classification strategy gives better precision than the approach of taking the product of the posterior probabilities.