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Hyper-Parameter in Hidden Markov Random Field
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 Title & Authors
Hyper-Parameter in Hidden Markov Random Field
Lim, Jo-Han; Yu, Dong-Hyeon; Pyu, Kyung-Suk;
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Hidden Markov random eld(HMRF) is one of the most common model for image segmentation which is an important preprocessing in many imaging devices. The HMRF has unknown hyper-parameters on Markov random field to be estimated in segmenting testing images. However, in practice, due to computational complexity, it is often assumed to be a fixed constant. In this paper, we numerically show that the segmentation results very depending on the fixed hyper-parameter, and, if the parameter is misspecified, they further depend on the choice of the class-labelling algorithm. In contrast, the HMRF with estimated hyper-parameter provides consistent segmentation results regardless of the choice of class labelling and the estimation method. Thus, we recommend practitioners estimate the hyper-parameter even though it is computationally complex.
Hidden Markov random field;hyper-parameter;image segmentation;
 Cited by
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