A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Knowledge Discovery in Spatial Databases
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Self-Supervised Learning for Visual Tracking and Recognition of Human Hand
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Text Detection in Images Based on Unsupervised Classification of High-Frequency Wavelet Coefficients
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Estimation of Arbitrary Camera Motion in MPEG Videos
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Self-Supervised Writer Adaptation using Perceptive Concepts: Application to On-Line Text Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Automatic Face Recognition for Film Character Retrieval in Feature-Length Films
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Video cut detection using frame windows
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Tracking concept drifting with an online-optimized incremental learning framework
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
(Un)Reliability of video concept detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A comparison of color features for visual concept classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Transductive multi-label learning for video concept detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Semi-Supervised Learning
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Scene extraction in motion pictures
IEEE Transactions on Circuits and Systems for Video Technology
Real-time shot change detection over online MPEG-2 video
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
Reliable video content analysis is an essential prerequisite for effective video search. An important current research question is how to develop robust video content analysis methods that produce satisfactory results for a large variety of video sources, distribution platforms, genres, and content. The work presented in this article exploits the observation that the appearance of objects and events is often related to a particular video sequence, episode, program, or broadcast. This motivates our idea of considering the content analysis task for a single video or episode as a transductive setting: the final classification model must be optimal for the given video only, and not in general, as expected for inductive learning. For this purpose, the unlabeled video test data have to be used in the learning process. In this article, a transductive learning framework for robust video content analysis based on feature selection and ensemble classification is presented. In contrast to related transductive approaches for video analysis (e.g., for concept detection), the framework is designed in a general manner and not only for a single task. The proposed framework is applied to the following video analysis tasks: shot boundary detection, face recognition, semantic video retrieval, and semantic indexing of computer game sequences. Experimental results for diverse video analysis tasks and large test sets demonstrate that the proposed transductive framework improves the robustness of the underlying state-of-the-art approaches, whereas transductive support vector machines do not solve particular tasks in a satisfactory manner.