A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pairwise Markov Random Fields and its Application in Textured Images Segmentation
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Models for learning spatial interactions in natural images for context-based classification
Models for learning spatial interactions in natural images for context-based classification
Unsupervised signal restoration using hidden Markov chains with copulas
Signal Processing
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional models for contextual human motion recognition
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Piecewise pseudolikelihood for efficient training of conditional random fields
Proceedings of the 24th international conference on Machine learning
Automatic Estimation and Removal of Noise from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Man-made structure detection in natural images using a causal multiscale random field
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Scene categorization using topic model based hierarchical conditional random fields
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Conditional random fields for land use/land cover classification and complex region detection
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Dynamic learning of SCRF for feature selection and classification of hyperspectral imagery
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases
Information Sciences: an International Journal
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Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. State-of-the-art hyperspectral image classification algorithms use the dependencies in a heuristic way or in probabilistic frameworks but impose unreasonable assumptions on observed data. In this paper, we formulate a conditional random field (CRF) to replace such heuristics and unreasonable assumptions for the classification of hyperspectral images. Moreover, because of avoiding explicit modeling of the observed data, the proposed method can incorporate the classification of hyperspectral images with different statistics characteristics into a unified probabilistic framework. Since the usual classification task for hyperspectral images needs the proposed CRF to be trained on local samples, available global training methods cannot be directly used. Under piecewise training framework, this paper develops an efficient local method to train the CRF. It is efficiently implemented through separated trainings of simple classifiers defined by corresponding potentials. However, the independent classifier trainings may lead to over-counting problems during inference. So we further propose a strategy to combine the independently trained models to obtain final CRF model. Experiments on real-world hyperspectral data show that our algorithm is competitive with the most recent results in hyperspectral image classification.