Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Similarity metric learning for a variable-kernel classifier
Neural Computation
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape quantization and recognition with randomized trees
Neural Computation
GTM: the generative topographic mapping
Neural Computation
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
Multiresolution tangent distance for affine-invariant classification
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Variational learning in nonlinear Gaussian belief networks
Neural Computation
Robustly estimating changes in image appearance
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Learning Graphical Models of Images, Videos and Their Spatial Transformations
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Transformed Component Analysis: Joint Estimation of Spatial Transformations and Image Components
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Competition and multiple cause models
Neural Computation
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Robust Parameterized Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Multimodal Data Representations with Parameterized Local Structures
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Graphical Model for Audiovisual Object Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust parameterized component analysis: theory and applications to 2D facial appearance models
Computer Vision and Image Understanding - Special issue on Face recognition
Translation-invariant mixture models for curve clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Distilling information with super-resolution for video surveillance
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Probabilistic index maps for modeling natural signals
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Model-Based Clustering for Large Databases Based on Data Summarization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised scene analysis: a hidden Markov model approach
Computer Vision and Image Understanding
Learning Parts-Based Representations of Data
The Journal of Machine Learning Research
Learning temporal structure for task based control
Image and Vision Computing
Fast Transformation-Invariant Component Analysis
International Journal of Computer Vision
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
International Journal of Computer Vision
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Unsupervised scene analysis: A hidden Markov model approach
Computer Vision and Image Understanding
Fast image registration with non-stationary Gauss-Markov random field templates
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Learning Real-Time Perspective Patch Rectification
International Journal of Computer Vision
Learning appearance and transparency manifolds of occluded objects in layers
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Unsupervised learning of multiple aspects of moving objects from video
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Probabilistic models for joint clustering and time-warping of multidimensional curves
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes
International Journal of Computer Vision
Learning deformations with parallel transport
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Continuous Generalized Procrustes analysis
Pattern Recognition
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Clustering is a simple, effective way to derive useful representations of data, such as images and videos. Clustering explains the input as one of several prototypes, plus noise. In situations where each input has been randomly transformed (e.g., by translation, rotation, and shearing in images and videos), clustering techniques tend to extract cluster centers that account for variations in the input due to transformations, instead of more interesting and potentially useful structure. For example, if images from a video sequence of a person walking across a cluttered background are clustered, it would be more useful for the different clusters to represent different poses and expressions, instead of different positions of the person and different configurations of the background clutter. We describe a way to add transformation invariance to mixture models, by approximating the nonlinear transformation manifold by a discrete set of points. We show how the expectation maximization algorithm can be used to jointly learn clusters, while at the same time inferring the transformation associated with each input. We compare this technique with other methods for filtering noisy images obtained from a scanning electron microscope, clustering images from videos of faces into different categories of identification and pose and removing foreground obstructions from video. We also demonstrate that the new technique is quite insensitive to initial conditions and works better than standard techniques, even when the standard techniques are provided with extra data.