automatic image segmentation by dynamic region growth and

8/3/2019 Automatic Image Segmentation by Dynamic Region Growth And

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Defined as classification of all the picture

elements or pixels in an image into different

clusters that exhibit similar features. Color , edges and textures are used as

properties.

Applications include object classification ,

image retrieval and medical imaging analysis,

brain tumor detection and fingerprinting.


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A new unsupervised color image

segmentation algorithm is proposed known as

G- SEGmentation algorithm. This algorithm exploits the information

obtained from detecting edges in color

images in CIE L*a*b* color space.

Pixels without edges are clustered and

labeled individually using color gradient

detection technique.


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Texture modeling is performed by color

quantization and local entropy of quantized

image. The obtained color and texture along with a

region growth map consisting of all fully

grown regions are used to perform a unique

multi-resolution merging procedure to

blend regions with similar characteristics.


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The detected areas with no edges inside them

are the initial clusters or seeds selected to

initiate the segmentation of the image. The pixels that compose each detected region

receive a label and the combination of pixels

with the same label is referred as a seed.

These seeds grow into the higher edge

density areas, and additional seeds are created

to generate an initial segmentation map.


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1. Selects clusters for images using gradient

information in CIE L*a*b* color space.

2. Characterizes the texture present in eachcluster.

3. Generates a final segmentation map by

utilizing an effective merging approach.


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Consists of three different modules as shown

in fig.1.

First module implements edge-detectionalgorithm to produce an edge map used in

generation of adaptive gradient thresholds,

which in turn dynamically select regions of

contiguous pixels that display similar

gradient and color values, producing an initial

segmentation map.


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The second module creates a texture

characterization channel by first quantizing

the input image, followed by entropy based

filtering of the quantized colors of the image.

The last module utilizes the initial

segmentation map and the texture channel to

obtain our final segmentation map.


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Here regions where the gradient map displays

no edges are searched.

The selected regions form the initial set ofseeds to segment the image.

The region growth procedure also accounts

for regions, which display similar edge values

throughout, by detecting unattached regions

at various edge density levels.


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Patterns are composed of multiple shades of colors

causing over-segmentation and misinterpretation of

the edges surrounding these regions.

Texture regions may contain regular patterns such as

a brickwall, or irregular patterns such as leopard

skins, bushes, and many objects found in nature.

A method for obtaining information of patternswithin an image is to evaluate the randomness

present in various areas of that image.

Entropy provides a measure of uncertainty of a

random variable


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Here an effective method is incorporated to analyze

grouped data from the statistical field, to merge all over

segmented regions.

This method better known as a multivariate analysisallows to take regions that have been separated due to

occlusion, or small texture differences, and merge them

together.

The core of a multivariate analysis lies in highlighting thedifferences between groups that display multiple variables to

investigate the possibility that multiple groups are associated

with a single factor.


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Using a multivariate analysis approach of all

independent regions, the resultant distances

between groups is used to merge similar

regions.

Since the image has been segmented into

different groups, information can be gathered

from each individual region.


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To prevent the need to re-evaluate the

distances for various groups after each stage

of the region merging procedure, an alternate

approach is introduced.

Having the distances between groups, the

smallest distance value is found,

corresponding to a single pair of groups.


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The similarity value is increased until a larger

set of group pairs is obtained.

The smallest group is merged first in this setand then continues to merge the next larger

group.

After the first merge, a check is performed to

see if one of the groups being merged is now

part of a larger group.


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In this case all the pair combinations of the

groups should belong to the pairs selected

initially in the set to be merged together.

Once all the pairs of the set have been

processed, the distance is recomputed for the

new segmentation map, and the process is

repeated.


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In problems such as segmentation, multi-resolution analysis offers two key advantagesover pixel-based methods:

1. It provides a way to trade-off class and spatialresolution; repeatedly blurring and sub-sampling the image decreases the noise andimproves the class certainty, but at the expenseof spatial resolution .


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2. The use of a multi-resolution technique ensures

both robustness in noise and efficiency

of computation.


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Result of G-SEG algorithm is as shown in

figure below.


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The input RGB image and its CIE L*a*b*

counterpart are shown in Fig. 2(a) and (b),

respectively.

The outcome of gradient computation on the colorconverted input image, shown in Fig. (2c).

The seed map at the end of the region growth

procedure, obtained utilizing thresholds that are

generated adaptively, is displayed in Fig. 2(d).


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The texture channel generated using color

quantization and local entropy calculation is

depicted in Fig. 2(e)

The segmentation map at the end of the

region merging algorithm is shown in Fig.

2(f).


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The Face image in Fig. 3(a) represents a

moderately complete image with dissimilar

texture content associated with the skin, hat

and robe of the person.

Observe that in Fig. 3(b) and 3(c), the GRF

and JSEG algorithms over segment this

image due to the texture and illuminationdisparity seen in various regions.


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The texture model has been effective in

handling different textures as seen in Fig.

3(d).

The algorithm employs the CIE L*a*b* color

space where the L* channel contains the

luminance information in the image,

incapacitates the illumination problem.


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The GSEG algorithm is primarily based on

color-edge detection, dynamic region growth,

and culminates in a unique multi-resolution

region merging procedure. The algorithm is

robust to various image scenarios and is

superior to the results obtained on the same

image when segmented by other methods.


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automatic image segmentation by dynamic region growth and

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