TY  - JOUR
T1  - Design and Implementations of Color Pixel Based Image Segmentation using Enhanced Data
Clustering Algorithms to Applying on Tiger Image Dataset
AU - Ramaraj, M. AU - Niraimathi, S. 
JO  - Research Journal of Animal Sciences
VL  - 14
IS  - 2
SP  - 16
EP  - 25
PY  - 2020
DA  - 2001/08/19
SN  - 1993-5269
DO  - rjnasci.2020.16.25
UR  - https://makhillpublications.co/view-article.php?doi=rjnasci.2020.16.25
KW  - Image segmentation
KW  -clustering algorithms
KW  -k-means
KW  -modified k-means
KW  -FBISODATA
KW  -FBDBSCAN and FBMC
AB  - Tiger has become the reserve animal.
Conservation of tiger has been the challenging task. This
research would add a small account to the herculean task
of conserving the species. This research proposes an
algorithm from which the age of the tiger can be inferred.
This research combines the domain of image processing
with data mining to infer the age of tiger. Image
processing techniques like image enhancement and
segmentation plays a vital role in mining the image of the
tiger. The image processing is complemented with data
mining to find the age of tiger where data mining plays
the role of analyzing the statistical report of confirming
the age of the tiger. Several scientific researchers have
carried out their research on the tiger reserve
conservation. This research work proposes a method to
find the age of the tiger, using color as a parameter. Color
pixel based image classification and clustering techniques
has been used to identify the age of the tiger. Clustering
is a part which considers the principal of systematic
techniques in handling. Clustering is the process of
making a group of abstract objects into classes of similar
objects. Image segmentation is the classification of an
image into different groups. Many researches have been
done in the area of image segmentation using clustering.
There are different methods and one of the most popular
methods is k-means clustering algorithm. In working on
k-mean clustering approach to cluster the data. Several
strategies have been proposed for enhancing the
performance of k-means clustering algorithm. DBSCAN
is designed to discover clusters of arbitrary shape.
DBSCAN which exploits its characteristics and at the
same time improves its limitation, so, it is used widely in
the clustering technique. The Mountain Clustering (FMC)
method is a relatively simple and effective approach to
approximate estimation of cluster centers on the basis of
a density measure. ISODATA algorithm (Iterative
Self-Organizing Data Analysis Technique Algorithm)
which allows the number of clusters to be adjusted
automatically during the iteration by merging similar
clusters and splitting clusters with large standard
deviations. The Modified k-Means Clustering (MKMC)
and Fuzzy ISODATA (FISODATA), FBDBSCAN,
FBMC cluster for making the algorithms much less time
consuming, greater high-quality and efficient for higher
clustering accuracy rate with reduction is time
complexity.
ER  -