Distinguishing photographs and graphics on the World Wide Web
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Action movies segmentation and summarization based on tempo analysis
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Detecting cartoons: a case study in automatic video-genre classification
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
A cartoon video detection method based on active relevance feedback and SVM
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Automatic Video Classification: A Survey of the Literature
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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We address a particular case of video genre classification, namely the classification of animated movies. This task is achieved using two categories of content descriptors, temporal and color based, which are adapted to this particular content. Temporal descriptors, like rhythm or action, are quantifying the perception of the action content at different levels. Color descriptors are determined using color perception which is quantified in terms of statistics of color distribution, elementary hues, color properties (e.g. amount of light colors, cold colors, etc.) and color relationship. The potential of the proposed descriptors to the classification task has been proved through experimental tests conducted on more than 159 hours of video footage. Despite the high diversity of the video material, the proposed descriptors achieve an average precision and recall ratios up to 90% and 92%, respectively, and a global correct detection ratio up to 92%.