Visual reconstruction
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Feature extraction from faces using deformable templates
International Journal of Computer Vision
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Efficient deformable template detection and localization without user initialization
Computer Vision and Image Understanding
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
CBF: A New Framework for Object Categorization in Cortex
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Image Parsing: Unifying Segmentation, Detection, and Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pictorial Structures for Object Recognition
International Journal of Computer Vision
A Coarse-to-Fine Strategy for Multiclass Shape Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Robust non-frontal face alignment with edge based texture
Journal of Computer Science and Technology
Composite Templates for Cloth Modeling and Sketching
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Shape Guided Object Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Context and Hierarchy in a Probabilistic Image Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A fast learning algorithm for deep belief nets
Neural Computation
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active mask hierarchies for object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Learning to combine bottom-up and top-down segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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This paper describes and reviews a class of hierarchical probabilistic models of images and objects. Visual structures are represented in a hierarchical form where complex structures are composed of more elementary structures following a design principle of recursive composition. Probabilities are defined over these structures which exploit properties of the hierarchy--e.g. long range spatial relationships can be represented by local potentials at the upper levels of the hierarchy. The compositional nature of this representation enables efficient learning and inference algorithms. In particular, parts can be shared between different object models. Overall the architecture of Recursive Compositional Models (RCMs) provides a balance between statistical and computational complexity.The goal of this paper is to describe the basic ideas and common themes of RCMs, to illustrate their success on a range of vision tasks, and to gives pointers to the literature. In particular, we show that RCMs generally give state of the art results when applied to a range of different vision tasks and evaluated on the leading benchmarked datasets.