C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Decomposition of Separable Concave Structuring Functions
Journal of Mathematical Imaging and Vision
The Morphological Structure of Images: The Differential Equations of Morphological Scale-Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-scale Document Description Using Rectangular Granulometries
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
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)
Automatic Genre Identification for Content-Based Video Categorization
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
A probabilistic multimedia retrieval model and its evaluation
EURASIP Journal on Applied Signal Processing
Physics-motivated features for distinguishing photographic images and computer graphics
Proceedings of the 13th annual ACM international conference on Multimedia
TV Genre Classification Using Multimodal Information and Multilayer Perceptrons
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Parallel neural networks for multimodal video genre classification
Multimedia Tools and Applications
An Enhanced Statistical Approach to Identifying Photorealistic Images
IWDW '09 Proceedings of the 8th International Workshop on Digital Watermarking
A temporal and visual analysis-based approach to commercial detection in news video
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Discriminating computer graphics images and natural images using hidden Markov tree model
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
A color-action perceptual approach to the classification of animated movies
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
IGC: an image genre classification system
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
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
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This paper presents a new approach for classifying individual video frames as being a 'cartoon' or a 'photographic image'. The task arose from experiments performed at the TREC-2002 video retrieval benchmark: 'cartoons' are returned unexpectedly at high ranks even if the query gave only 'photographic' image examples. Distinguishing between the two genres has proved difficult because of their large intra-class variation. In addition to image metrics used in prior cartoon-classification work, we introduce novel metrics like ones based on the pattern spectrum of parabolic size distributions derived from parabolic granulometries and the complexity of the image signal approximated by its compression ratio. We evaluate the effectiveness of the proposed feature set for classification (using support vector machines) on a large set of keyframes from the TREC-2002 video track collection and a set of Web images. The paper reports the identification error rates against the number of images used as training set. The system is compared with one that classifies Web images as photographs or graphics and its superior performance is evident.