Statistical Image Modeling and Processing Using Wavelet-Domain Hidden Markov Models - Nonlinear Signal and Image Processing: Theory, Methods and Applications - page 358

Ω

Ω

NP P

N NP P

P
V1 V2 V1 V3V2 V1

Ω

Context-1 Context-2 Context-3

(a) (b) (c)

N
V1

P

N
V1

V2

Ω Ω

Context-4

Context-5

(d) (e)

FIGURE 10.10
Five context models between two neighboring scales where the coarser scale (top) and finer
scale (bottom) are shown. { V 1 , ... ,V d } ( d = 1 , 2 , 3 ) is the context vector, and is defined in

Equation 10.31.

The five context models are depicted in Figure 10.10, among which
Context-1 and Context-2 are interscale context models, which are similar to
those used to encourage the formation of large, uniformly classified regions. 7 , 33
Context-5 is an intrascale context model often used in the MRF literature
to ensure local homogeneous labeling with high sensitivity to boundaries.
Context-3 and Context-4 are hybrid inter- and intrascale context models,
which have similar characteristics to those used in References 37 through 39.
We anticipate that those context models have distinct effects on the segmen-
tation results in terms of classification, boundary localization, and boundary
detection. To study their characteristics, we use three numerical criteria to
quantify the segmentation performance. Specifically, P a is the percentage of
pixels that are correctly classified, showing accuracy , P b the percentage of