Journal article
Maximum likelihood unsupervised textured image segmentation
CVGIP. Graphical models and image processing, v 54(3)
1992
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper presents an algorithm for segmenting an image that is composed of an unknown number of regions
c. In each region
n, the image data g
n
are viewed as a realization from a homogeneous parametric random field with a class conditional density function
p(g
n
|
γ
n
), where
γ
n
is an unknown parameter set. The number of regions
c and the segmentation S
c
are treated as unknown constants that are estimated using the maximum likelihood (ML) estimation principle. The ML estimates for
c and S
c
are obtained by maximizing log{
p(g|S
c)} over all possible
c and S
c
.
p(g|S
c) has the desirable property of unbiasedness; i.e.,
E
S
ctrue
{log{
p(g|S
c
)}} ≤
E
S
ctrue
{log{
p(g|S
ctrue
)}. Unfortunately, it suffers from two limitations: (i) a closed-form analytic expression for
p(g|S
c
) for a given fixed
c cannot be obtained in general, and (ii) in order to arrive at the optimum (
c
∗,
S
c
∗
) we must evaluate
p(g|S
c) for all possible
c and S
c
, a most formidable task. This paper presents a solution to both problems that results into an optimum number of classes
c
∗
; an “optimum” window-based coarse segmentation
S
c
∗
of the image; and a ML estimate of the parameters
Γ
S
∗c
∗
= (γ
1, γ
2, …, γ
c
∗
)
of the
c
∗
regions induced by
S
c
∗
. From this knowledge, the mixed windows (windows that fall between regions) are segmented further in a supervised mode (known parameter case) using the ML high-resolution segmentation developed by Cohen and Cooper (
IEEE Trans. Pattern Anal. Mach. Intelligence Mar., 1987). The ML algorithm is applied to the problem of unsupervised segmentation of textured images of natural outdoor scenes.
Metrics
Details
- Title
- Maximum likelihood unsupervised textured image segmentation
- Creators
- Fernand S Cohen - Drexel UniversityZhigang Fan - Xerox Webster Research Center, 800 Philips Road, 128-29E, Webster, New York 14580 USA
- Publication Details
- CVGIP. Graphical models and image processing, v 54(3)
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:A1992HR05700005
- Scopus ID
- 2-s2.0-0026451901
- Other Identifier
- 991019173887704721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Software Engineering