Camera Motion Extraction Using Correlation for Motion-Based Video Classification
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Automatic Sports Video Genre Classification using Pseudo-2D-HMM
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Parallel neural networks for multimodal video genre classification
Multimedia Tools and Applications
Automatic sports genre categorization and view-type classification over large-scale dataset
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A semantic framework for video genre classification and event analysis
Image Communication
Content-based video genre classification using multiple cues
Proceedings of the 3rd international workshop on Automated information extraction in media production
Violence Detection in Movies with Auditory and Visual Cues
CIS '10 Proceedings of the 2010 International Conference on Computational Intelligence and Security
Content-based video description for automatic video genre categorization
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Automatic Video Classification: A Survey of the Literature
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Short user-generated videos classification using accompanied audio categories
Proceedings of the 2012 ACM international workshop on Audio and multimedia methods for large-scale video analysis
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As the increasing affordability for capturing and storing video and the proliferation of Web 2.0 applications, video content is no longer necessarily created and supplied by a limited number of professional producers; any amateur can produce and publish his/her video quickly. Therefore, the amount of both professional-produced as well as amateur-produced video on the web is ever increasing. In this work, we propose a question; whether we can automatically classify an Internet video clip as being either professional-produced or amateur-produced? Hence, we investigate features and classification methods to answer this question. Based on the differences in the production processes of these two video categories, four features including camera motion, structure, audio feature and combined feature are adopted and studied along with with four popular classifiers KNN, SVM GMM and C4.5. Extensive experiments over representative datasets, evaluate these features and classifiers under different settings and compare to existing techniques. Experimental results demonstrate that SVMs with multimodal features from multi-sources are more effective at classifying video type. Finally, for answering the proposed question, results also show that automatically classifying a clip as professional-produced video or amateur-produced video can be achieved with good accuracy.