Fast training of support vector machines using sequential minimal optimization
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Robust Real-Time Face Detection
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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
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
A unified framework for semantic shot representation of sports video
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Fusion of AV features and external information sources for event detection in team sports video
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Self-Supervised Learning of Face Appearances in TV Casts and Movies
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Event detection in field sports video using audio-visual features and a support vector Machine
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A naive mid-level concept-based fusion approach to violence detection in Hollywood movies
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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In this paper, we present an automatic semantic video analysis system to support interdisciplinary research efforts in the field of psychology and media science. The psychological research question studied is whether and how playing violent content in computer games may induce aggression. To investigate this question, the extraction of meaningful content from computer games is required to gain insights into the interrelationship of violent game events and the underlying neurophysiologic basis (brain activity) of a player. Previously, human annotators had to index game content according to the current game state, which is a very time-consuming task. The automatic annotation of a large number of computer game recordings (i.e. videos) speeds up the experimentation process and allows researchers to analyze more experimental data on an objective basis. The proposed computer game video content analysis system for computer games extracts several audiovisual low-level as well as mid-level features and deduces semantic content via a machine learning approach. This system requires manual annotations for a single video only to facilitate the semi-supervised learning process. Finally, human experts are allowed to refine the annotation results via a graphical user interface. Experimental results demonstrate the feasibility of the proposed approach.