using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks

Full Title

Arrangements for image or video recognition or understanding > using pattern recognition or machine learning > using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks

Classification Context

Section:

PHYSICS

Class:

COMPUTING OR CALCULATING; COUNTING

Subclass:

IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

Scope Notes

Glossary: EM expectation maximisation, iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. HMM hidden Markov model, statistical Markov model in which the system being modelled is assumed to be a Markov process with unobservable ("hidden") states. PLSA probabilistic latent semantic analysis, a representation model in which the probability of co-occurrence of data is modelled as a mixture of conditionally independent multinomial distributions.