Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Assessing concept selection for video retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Foundations and Trends in Information Retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Video event classification using string kernels
Multimedia Tools and Applications
Video corpus annotation using active learning
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
Pegasos: primal estimated sub-gradient solver for SVM
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
Personalizing automated image annotation using cross-entropy
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Selection of Concept Detectors for Video Search by Ontology-Enriched Semantic Spaces
IEEE Transactions on Multimedia
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
A semantic event-detection approach and its application to detecting hunts in wildlife video
IEEE Transactions on Circuits and Systems for Video Technology
Semantic Model Vectors for Complex Video Event Recognition
IEEE Transactions on Multimedia
Action bank: A high-level representation of activity in video
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Multimodal feature fusion for robust event detection in web videos
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Visual Event Recognition in Videos by Learning from Web Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection bank: an object detection based video representation for multimedia event recognition
Proceedings of the 20th ACM international conference on Multimedia
Proceedings of the 21st ACM international conference on Multimedia
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An emerging trend in video event detection is to learn an event from a bank of concept detector scores. Different from existing work, which simply relies on a bank containing all available detectors, we propose in this paper an algorithm that learns from examples what concepts in a bank are most informative per event. We model finding this bank of informative concepts out of a large set of concept detectors as a rare event search. Our proposed approximate solution finds the optimal concept bank using a cross-entropy optimization. We study the behavior of video event detection based on a bank of informative concepts by performing three experiments on more than 1,000 hours of arbitrary internet video from the TRECVID multimedia event detection task. Starting from a concept bank of 1,346 detectors we show that 1.)some concept banks are more informative than others for specific events, 2.) event detection using an automatically obtained informative concept bank is more robust than using all available concepts, 3.) even for small amounts of training examples an informative concept bank outperforms a full bank and a bag-of-word event representation, and 4.) we show qualitatively that the informative concept banks make sense for the events of interest, without being programmed to do so. We conclude that for concept banks it pays to be informative.