The nature of statistical learning theory
The nature of statistical learning theory
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Inference of Non-Overlapping Camera Network Topology by Measuring Statistical Dependence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Multimodal Genre Analysis Applied to Digital Television Archives
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
Analyzing the video popularity characteristics of large-scale user generated content systems
IEEE/ACM Transactions on Networking (TON)
Indoor vs. outdoor scene classification in digital photographs
Pattern Recognition
Computer Vision and Image Understanding
Crowdsourced automatic zoom and scroll for video retargeting
Proceedings of the international conference on Multimedia
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
The evaluation of the error characteristics of multiple GPS terminals
CSCS '11 Proceedings of the 2nd international conference on Circuits, systems, control, signals
Multimodal Event Detection in User Generated Videos
ISM '11 Proceedings of the 2011 IEEE International Symposium on Multimedia
Automatic Video Classification: A Survey of the Literature
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Proceedings of the 4th ACM Multimedia Systems Conference
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User-generated video content has grown tremendously fast to the point of outpacing professional content creation. In this work we develop methods that analyze contextual information of multiple user-generated videos in order to obtain semantic information about public happenings (e.g., sport and live music events) being recorded in these videos. One of the key contributions of this work is a joint utilization of different data modalities, including such captured by auxiliary sensors during the video recording performed by each user. In particular, we analyze GPS data, magnetometer data, accelerometer data, video- and audio-content data. We use these data modalities to infer information about the event being recorded, in terms of layout (e.g., stadium), genre, indoor versus outdoor scene, and the main area of interest of the event. Furthermore we propose a method that automatically identifies the optimal set of cameras to be used in a multicamera video production. Finally, we detect the camera users which fall within the field of view of other cameras recording at the same public happening. We show that the proposed multimodal analysis methods perform well on various recordings obtained in real sport events and live music performances.