Learning Graphical Models of Images, Videos and Their Spatial Transformations

by   Brendan J. Frey, et al.

Mixtures of Gaussians, factor analyzers (probabilistic PCA) and hidden Markov models are staples of static and dynamic data modeling and image and video modeling in particular. We show how topographic transformations in the input, such as translation and shearing in images, can be accounted for in these models by including a discrete transformation variable. The resulting models perform clustering, dimensionality reduction and time-series analysis in a way that is invariant to transformations in the input. Using the EM algorithm, these transformation-invariant models can be fit to static data and time series. We give results on filtering microscopy images, face and facial pose clustering, handwritten digit modeling and recognition, video clustering, object tracking, and removal of distractions from video sequences.


page 1

page 3

page 4

page 5

page 6

page 7

page 8


Model-based clustering with Hidden Markov Model regression for time series with regime changes

This paper introduces a novel model-based clustering approach for cluste...

Graphical Models for Financial Time Series and Portfolio Selection

We examine a variety of graphical models to construct optimal portfolios...

K-ARMA Models for Clustering Time Series Data

We present an approach to clustering time series data using a model-base...

Products of Hidden Markov Models: It Takes N>1 to Tango

Products of Hidden Markov Models(PoHMMs) are an interesting class of gen...

On Learning Prediction-Focused Mixtures

Probabilistic models help us encode latent structures that both model th...

Time Adaptive Gaussian Model

Multivariate time series analysis is becoming an integral part of data a...

Please sign up or login with your details

Forgot password? Click here to reset