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A Review of Hidden Markov Models for Context-Based Classification
Historical Note
• “Classification in Context” was well-studied in pattern recognition in the 60’s and 70’s
– e.g, recursive Markov-based algorithms were proposed, before hidden Markov algorithms and models were fully understood
• Applications in
– OCR for word-level recognition
– remote-sensing pixel classification
Context-Based Classification Problems
• Medical Diagnosis
– classification of a patient’s state over time
– Fraud Detection
– detection of stolen credit card
– Electronic Nose
– detection of landmines
– Remote Sensing
– classification of pixels into ground cover
Modeling Context
• Common Theme = Context
– class labels (and features) are “persistent” in time/space
Brief review of hidden Markov models (HMMs)
Graphical Models
• Basic Idea: p(U) <=> an annotated graph
– Let U be a set of random variables of interest
– 1-1 mapping from U to nodes in a graph
– graph encodes “independence structure” of model
– numerical specifications of p(U) are stored locally at the nodes
• Acyclic Directed Graphical Models (aka belief/Bayesian networks)
Undirected Graphical Models (UGs)
• Undirected edges reflect correlational dependencies
– e.g., particles in physical systems, pixels in an image
• Also known as Markov random fields, Boltzmann machines, etc
Approach and Results
• Classifiers
– Gaussian model and neural network
– trained on labeled “instantaneous window” data
• Markov component
– transition probabilities estimated from MTBF data
– Results
– discriminative neural net much better than Gaussian
– Markov component reduced the error rate (all false alarms) of 2% to 0%.
Classification with and without the Markov context