Solviing Vision Problem by Inference using Graphical Generative Model
Many difficult vision problems cannot be easily solved using traditional methods. A research group in the university of toronto lead by professor Brendan J. Frey and Nebojsa Jojic (Microsoft Research) proposed a set of theories and models based on probability inference for these hard problems from 1999 to date.
This topic is known as "Inference and Learning in Graphical Generative Model". The basic knowledge of this framework is as follows:
- Model the target problem using probability and then build the generative model such as (Bayes Network); In details, the modeling process begins with analysis of the dependence relationship of each (hidden) variable and observation, then model the probabilities of independent variables according to some assumption (such as gaussian). In the next step, the conditional probability of dependent variables are modeled also according to some assumptions. Finally the joint distribution of overservation as well as all variables are modeled.
- According to the target problem, the posterior probability of some interested hidden variables given the ovservations are infered to come out the fomulas;
- The problem is transformed to parameter estimation. Once the parameters appearing in the fomula of posterior probability are estimated, the values of hidden variable can be known; This can be achieved by using such as EM algorithm;
Interesting things, need further study!!!!
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