Overlap Wavelet Transform for Image Segmentation

International Journal of Electronics Communication and Computer Technology (IJECCT)
Volume 4 Issue 3 (May 2014)
Overlap Wavelet Transform for Image Segmentation
A.S.Senthilkani, Christo Ananth
Praghash.k, Chakka Raja.M.
Jerrin John, I.Annadurai
Assistant Professor,
Department of ECE
Francis Xavier Engineering College
Tirunelveli, Tamil Nadu, India
PG Scholar, Department of
Communication System
Francis Xavier Engineering College
Tirunelveli, Tamil Nadu, India
PG Scholar,
Department of VLSI
Francis Xavier Engineering College
Tirunelveli, Tamil Nadu, India
Abstract—A new color image segmentation approach based on
OWT is presented in this work. OWT extracts wavelet features
which give a good separation of different patterns. Moreover the
proposed algorithm uses morphological operators for effective
segmentation. From the qualitative and quantitative results, it is
concluded that our proposed method has improved segmentation
quality and it is reliable, fast and can be used with reduced
computational complexity than direct applications of Histogram
Clustering. The main advantage of this method is the use of
single parameter and also very faster. While comparing with five
color spaces, segmentation scheme produces results noticeably
better in RGB color space compared to all other color spaces.
image. The maximum energy sub-bands are selected for
clustering. The advantage of this approach is the features from
different levels of resolution are combined since all input
images are of same size.
Input Image
Split into four Sub-Images
Keywords-- Overlap Wavelet transform; Histogram
Clustering;Edge Based Methods; Region Based Methods.
SWT
I.
INTRODUCTION
The goal of Image Segmentation is grouping of pixels into
meaningful objects. The quality of Image segmentation
depends upon the image. The traditional K-means algorithm
has the disadvantage of the number of clusters must be applied
as a parameter. Direct applications of 2D Histogram Clustering
fail to segment all the regions. Hence, OWT based 2D
histogram Clustering is used. In the first stage, OWT is used to
introduce redundancy in the filtered images which produce
reliable result in segmentation process. The second stage 2D
histogram is used to find the peaks without any prior
knowledge. The main peak reflects the cluster centroids. The
third stage, Label Concordance algorithm is used to refine the
extracted regions based on local and global information.
II.
MATERIALS AND METHODS
A. Overlap Wavelet Transform (OWT)
An adaptive window size to generate wavelet features is
proposed. For n level n, the original input image is split into
4n sub-images. Here the input image is split into 4 subimages. Then each image is subjected to stationary wavelet
transform (SWT). The outputs of the same kind of filter
images are interleaved to form four images of same size as the
original image. The Schematic diagram of OWT is shown in
figure.1.The final feature image is constructed using odd rows
and odd columns of the first image, odd rows and even
columns of the second image, even rows and odd columns of
the third image and even rows and even columns of the fourth
ISSN:2249-7838
Interleav
ed output
Interleav
ed output
Interleav
ed output
Interleav
ed output
Figure 1. Schematic diagram of OWT
B. Histogram Clustering algorithm
This algorithm consists of three main stages.
1) Clustering of Color planes
2) Label Concordance Mapping
3) Majority Filtering.
By Clustering of Color Planes, the band subsets are chosen
as RG, RB, and GB pairs.2D histogram is constructed by
summing up all the intensities occurring in the plane. The main
peaks of the histogram give the cluster centroids. Due to
sparseness of the colors in the image, the histogram is noisy.
An exponential filter is applied, to remove the noise and
smoothen the histogram.
IJECCT | www.ijecct.org
656
International Journal of Electronics Communication and Computer Technology (IJECCT)
Volume 4 Issue 3 (May 2014)
To speed up the cluster determination, the noiseless
histogram is down sampled by a factor 2 i.e. if the smoothed
histogram is of the size 256 X 256,it is reduced to 128 X
128.The down sampling is done by removing the neighborhood
value by the mean value of the two pixel (bin). To extract the
dominant colors, an erosion set is applied which reduces each
bin to its main colors. This method directly extracts the color
cluster centroids. The centroids are labeled and a voronoi
partitioning of the 2D histogram provides the clustering of the
histogram. The clustered histogram is finally up sampled to its
original size by replication of the pixels.
C. Label Concordance algorithm:
To unify the segmentation maps, Label matching algorithm
is used. Each image pair was segmented independently and
labeled. Label Transformation is used to match the labels of
segmentation map I to co-located segments in another map j on
the basis of maximum mutual overlap is defined as follows.
Tij(x) =y
(1)
Where x denotes the source label in segmentation map i and
y denotes the target label in segmentation map j and T ij is the
label transformation. This equation shows that the region label
x in map i must be same as label y in map j on the basis of
being co-located and maximally overlapping. Thus six
transformations
are
formed.
TRG,RB
,TRG,GB
,
TRB,RG,TRB,GB,TGB,RG,TGB,RB . Using these definitions of
transformations, bilateral matching cases are checked to find
out regions to be identically labeled. A match is defined as,
Tji(Tij(x))=x
(a)
RG
(2)
This equation means that in map i and j , there are two
segments that are each others maximally overlapping
counterparts , so that the x-labeled segment in i is mapped into
y in j , while the y-segment in j mapped to x in i. Notice that in
general if Tij(x)=y, then Tji(y)≠x.
III.
GB
(b)
EXPERIMENTAL WORK
The proposed algorithm is applied on the variety of natural
color images and it is tested on various color spaces. Figure 4
explains the entire process of the proposed algorithm. The
input color image shown in Fig.2.(a) is subjected to 2D
histogram clustering to obtain the clustered image. Initially
color image is splitted into three planes (R, G, B) and 2D
histogram of RG, RB, GB planes are calculated which are
depicted in Fig.2.(b). Then the histogram is smoothed by
Gaussian filter with standard deviation 0.625 and downsampled by a factor of 2. Smoothed and down-sampled
versions of the 2D histograms are illustrated in Fig.2.(c).
Morphological erosion is applied on the smoothed histogram
which directly extracts the cluster centroids i.e , dominant
peaks in the 2D histogram which is shown in Fig.2.(d). These
centroids are labeled and watershed transform of the 2D
histogram is performed which provides the clustered histogram
shown in Fig.2.(e). From the clustered histogram the
segmentation map is obtained from simple mapping. Then the
label concordance transformation is performed in order to unify
the segmentation maps which are illustrated in Fig.2.(f).
Unified segmentation maps are fused by using spatialchromatic majority filtering which gives the final segmented
result. Fig.2.(g) shows the segmented result.
ISSN:2249-7838
RB
RG
RB
GB
(c)
IJECCT | www.ijecct.org
657
International Journal of Electronics Communication and Computer Technology (IJECCT)
Volume 4 Issue 3 (May 2014)
Table I shows the performance of OWT with
Histogram Clustering with respect to traditional Histogram
Clustering approach. The Performance analysis shows that the
Overlap wavelet transform outsmarts the traditional Histogram
Clustering approach in terms of Color Error and Evaluation
function
IV.
RG
CONCLUSION
A new color image segmentation approach based on OWT
is presented in this work. OWT extracts wavelet features
which give a good separation of different patterns. Moreover
the proposed algorithm uses morphological operators for
effective segmentation. From the qualitative and quantitative
results, it is concluded that our proposed method has improved
segmentation quality and it is reliable, fast and can be used
with reduced computational complexity than direct
applications of Histogram Clustering. The main advantage of
this method is the use of single parameter and also very faster.
While comparing with five color spaces, segmentation scheme
produces results noticeably better in RGB color space
compared to all other color spaces.
RB
GB
REFERENCES
(d)
[1]
[2]
[3]
[4]
[5]
[6]
(e)
M.Celenk, “A color clustering technique for image segmentation.
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on Image Processing, Vol no.52,Issue 4,pp-145-170, 2012
J.Postaire, R.Zhang and C.Leococq-Bottle, “Cluster analysis by binary
morphology”, IEEE Transactions on pattern analysis and machine
Intelligence, Vol no.15 (2): pp-170-180,2011.
Park , I. Yun, and S. Lee, “ Color image segmentation based on 3-d
clustering : morphological approach. Pattern Recognition”,Vol.no.
31(8):pp-1061-1076,2008
Eduardo Akira Yonekura, and Jacques Facon, “Postal Envelope
Segmentation by 2-D Histogram Clustering through Watershed
Transform”, In proceedings of ICDAR 2010.
J. Serra, “Image Analysis and mathematical morphology”, Academic
press, London, 2009.
M P.Soille, “Morphological partitioning of multispectral images”,
Journal of Electronic Imaging,, Vol 18(4):pp- 252-265, 2007
Figure 2. Steps involved in proposed method (a) Input image (b)
Histograms with Labelled Centroids (c)Watershed transformed (or)
Clustered histograms (d) Segmentation maps (e) Segmented result
PROPOSED METHOD VS HISTOGRAM CLUSTERING
METHOD
ISSN:2249-7838
0.9522
Eval
Function
0.2964
Histogram Clustering
Color
Error
5
Eval
Function
Color
Error
Hill
No of
Regions
Input
Image
OWT with Histogram
Clustering
No of
Regions
TABLE I.
3
0.5938
1.3224
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