A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action movies segmentation and summarization based on tempo analysis
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Video genre classification using dynamics
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Fuzzy color-based approach for understanding animated movies content in the indexing task
Journal on Image and Video Processing - Color in Image and Video Processing
Parallel neural networks for multimodal video genre classification
Multimedia Tools and Applications
An Approach to the Parameterization of Structure for Fast Categorization
International Journal of Computer Vision
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
A motion-tolerant dissolve detection algorithm
IEEE Transactions on Multimedia
Automatic Video Classification: A Survey of the Literature
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hi-index | 0.00 |
We address the issue of automatic video genre retrieval. We propose three categories of content descriptors, extracted at temporal, color and structural level. At temporal level, video content is described with visual rhythm, action content and amount of gradual transitions. Colors are globally described with statistics of color distribution, elementary hues, color properties and relationship. Finally, structural information is extracted at image level and histograms are built to describe contour segments and their relations. The proposed parameters are used to classify 7 common video genres, namely: animated movies/cartoons, commercials, documentaries, movies, music clips, news and sports. Experimental tests using several classification techniques and more than 91 hours of video footage prove the potential of these parameters to the indexing task: despite the similarity in semantic content of several genres, we achieve detection ratios ranging between 80−100%.