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
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A robust audio classification and segmentation method
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Media Computing: Computational Media Aesthetics
Media Computing: Computational Media Aesthetics
Robust Real-Time Face Detection
International Journal of Computer Vision
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Semi-Supervised Cross Feature Learning for Semantic Concept Detection in Videos
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Tracking concept drifting with an online-optimized incremental learning framework
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Robust Scene Categorization by Learning Image Statistics in Context
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Multimedia semantic indexing using model vectors
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Self-Supervised Learning of Face Appearances in TV Casts and Movies
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Video motion representation for improved content access
IEEE Transactions on Consumer Electronics
IEEE Transactions on Circuits and Systems for Video Technology
Adapting appearance models of semantic concepts to particular videos via transductive learning
Proceedings of the international workshop on Workshop on multimedia information retrieval
Semi-supervised learning of object categories from paired local features
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Proceedings of the ACM International Conference on Image and Video Retrieval
Request/response aspects for web services
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
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The automatic understanding of audiovisual content for multimedia retrieval is a difficult task, since the meaning respectively the appearance of a certain event or concept is strongly determined by contextual information. For example, the appearance of a high-level concept, such as e.g. maps or news anchors, is determined by the used editing layout which usually is typical for a certain broadcasting station. In this paper, we show that it is possible to adaptively learn the appearance of certain objects or events for a particular test video utilizing unlabeled data in order to improve a subsequent retrieval process. First, an initial model is obtained via supervised learning using a set of appropriate training videos. Then, this initial model is used to rank shots for each test video v separately. This ranking is used to label the most relevant and most irrelevant shots in a video v for subsequent use as training data in a semi-supervised learning process. Based on these automatically labeled training data, relevant features are selected for the concept under consideration for video v. Then, two additional classifiers are trained on the automatically labeled data of this video. Adaboost and Support Vector Machines (SVM) are incorporated for feature selection and ensemble classification. Finally, the newly trained classifiers and the initial model form an ensemble. Experimental results on TRECVID 2005 video data demonstrate the feasibility of the proposed learning scheme for certain high-level concepts.