Content-based browsing of video sequences
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Feature detector and descriptor evaluation in human action recognition
Proceedings of the ACM International Conference on Image and Video Retrieval
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Human action segmentation and recognition via motion and shape analysis
Pattern Recognition Letters
Relevance feedback for real-world human action retrieval
Pattern Recognition Letters
Content-based retrieval of human actions from realistic video databases
Information Sciences: an International Journal
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Content-based retrieval systems are becoming increasingly relevant for managing large multimedia databases, such as those found on the Internet. In this paper, we investigate applying content-based retrieval with relevance feedback to the popular YouTube human action dataset[8], using a variety of methods to extract and compare features, in order to determine the most accurate techniques in this setting. Among other techniques, we explore soft-assignment code-book clustering, feature pruning, motion and static features, Adaboost and ABRS-SVM for relevance feedback. We evaluate the performance of several different systems to find the best combination of techniques for human action retrieval. We demonstrate that existing relevance feedback methods do not work well for YouTube media, and that a naive algorithm consistently outperforms these.