Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
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We present an image classification system based on a Conditional Random Field (CRF) model trained on simple features obtained from a small number of semantically representative image patches. The CRFs are very powerful to handle complex parts dependencies due to their approach based on the effective modelling of the source probability conditioned on the evidence data, and they have been applied successfully to image classification and segmentation tasks in presence of a large number of low level features. In this paper an agile system based on the application of CRFs to images coarsely segmented is introduced. The main advantage of the system is a reduction in the required training time, slightly sacrificing the classification accuracy. The model implementation is described, experimental results are presented and conclusions are drawn.