Connectionist learning of belief networks
Artificial Intelligence
A comparison of approaches to on-line handwritten character recognition
A comparison of approaches to on-line handwritten character recognition
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Training structural SVMs when exact inference is intractable
Proceedings of the 25th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
Image annotation via graph learning
Pattern Recognition
Learning a two-stage SVM/CRF sequence classifier
Proceedings of the 17th ACM conference on Information and knowledge management
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Search-based structured prediction
Machine Learning
Cutting-plane training of structural SVMs
Machine Learning
A formal framework for speedup learning from problems and solutions
Journal of Artificial Intelligence Research
Large margin Boltzmann machines
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Automated image annotation using global features and robust nonparametric density estimation
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
A new class of upper bounds on the log partition function
IEEE Transactions on Information Theory
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
Guest Editorial: Special Issue on Structured Prediction and Inference
International Journal of Computer Vision
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Images usually contain multiple objects that are semantically related to one another. Mapping from low-level visual features to mutually dependent high-level semantics can be formulated as a structured prediction problem. Current statistical models for structured prediction make simplifying assumptions about the underlying output graph structure, such as assuming a low-order Markov chain, because exact inference becomes intractable as the tree-width of the underlying graph increases. Approximate inference algorithms, on the other hand, force one to trade off representational power with computational efficiency. In this paper, we present large margin sigmoid belief networks (LMSBNs) for structured prediction in images. LMSBNs allow a very fast inference algorithm for arbitrary graph structures that runs in polynomial time with high probability. This probability is data-distribution dependent and is maximized in learning. The new approach overcomes the representation-efficiency trade-off in previous models and allows fast structured prediction with complicated graph structures. We present results from applying a fully connected model to semantic image annotation, image retrieval and optical character recognition (OCR) problems, and demonstrate that the proposed approach can yield significant performance gains over current state-of-the-art methods.