International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 5 (2018) pp. 2484-2489
© Research India Publications. http://www.ripublication.com
A Review of Image Segmentation using Clustering Methods
Sandeep Kumar Dubey
Department Computer Science and Engineering
Shri Ramswaroop Memorial Group of Professional Colleges -SRMGPC, Lucknow, India.
Dr.Sandeep Vijay
Department of Electronics and Electrical Engineering
ICFAI University, Dehradun, India.
Pratibha
Department of Electronics and Electrical Engineering
Ambalika Institute of Management and Technology -AIMT, Lucknow, India
Abstract
Division is an expansive term, covering a wide assortment of
issues and of strategies. We have gathered a delegate set of
thoughts in this paper. These strategies manage various types
of informational collection: some are expected for pictures,
some are proposed for video successions and some are
planned to be connected to tokens put holders that
demonstrate the nearness of a fascinating example, say a spot
or a speck or an edge point. While externally these strategies
may appear to be very changed, there is a solid likeness among
them. Every strategy endeavours to get a minimized portrayal
of its informational index utilizing some type of model of
similitude .One regular perspective of division is that we are
endeavouring to figure out which parts of an informational
collection normally "has a place together". This paper surveys
the picture division strategies in view of grouping. From the
surveys unmistakably grouping assumes an imperative part in
picture division.
Figure 1: Steps of Segmentation
Picture partition doling out a level to each pixel in a picture to
such an extent that pixels with a similar name share certain
qualities.
Keywords: Image Segmentation, Video Segmentation,
Cluster technique, K-means, Fuzzy C-means, Hierarchical
clustering, Mixture of Gaussians, Artificial neural network
clustering
Clustering is the undertaking of collection an arrangement of
articles such that items in a similar gathering (called a cluster)
are more comparative (in some sense or another) to each other
than to those in dissimilar gatherings (groups). Bunch
examination itself isn't one particular calculation, yet the
general assignment to be unraveled. It can be accomplished by
different calculations that vary essentially in their idea of what
constitutes a group and how to effectively discover them.
Prominent ideas of bunches incorporate gatherings with little
separations among the group individuals, thick zones of the
information space, interims or specific measurable
circulations. Grouping can thusly be detailed as a multi-target
enhancement issue. Group examination all things considered
isn't a programmed task, yet an iterative procedure of
information revelation or intuitive multi-target progression
that includes trial and disappointment. It is frequently
important to change information pre processing and
demonstrate parameters until the point when the outcome
accomplishes the coveted properties.
INTRODUCTION
Image segmentation can be defined as the arrangement of all
the picture elements or pixels in an image into dissimilar
clusters that demonstrate similar features. Segmentation
involves partitioning an image into groups of pixels which are
uniform with respect to some norm. Diverse gatherings must
not meet each other and adjoining bunches must be
heterogeneous. Picture division is considered as an imperative
fundamental operation for important investigation and
understanding of picture obtained. It is a basic and
fundamental part of a picture investigation or potentially
design acknowledgment framework, and is a standout among
the most troublesome undertakings in picture preparing, which
decides the nature of the last division.
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 5 (2018) pp. 2484-2489
© Research India Publications. http://www.ripublication.com
CLASSIFICATION OF IMAGE CLUSTERING:
Brief Review of the Previous Work :
Image clustering identifies with content-based picture
recovery frameworks. It empowers the usage of proficient
recovery calculations and the production of an easy to use
interface to the database. There are several clustering
techniques:
Pillai et al [4] proposes picture steganography strategy which
bunches the picture into different portions and conceals
information in each of the section. A mix of cryptographic and
bunching procedures has been utilized alongside
steganography. This gives significantly more prominent
security as the encryption key is likewise obscure to the
assailant.
Mathur et al [13] proposes fuzzy based edge location utilizing
K-means bunching strategy. The K-means bunching approach
is utilized as a part of producing different gatherings which are
then contribution to the mamdani fuzzy deduction framework.
Reproduction comes about speak to that the K-means
grouping strategy for the age of edge parameters in the fuzzy
based framework enhances the edge picture for MRI picture.
Yadav et al [3] proposes a neoteric picture division procedure
has been encircled, which is remained on shade of the picture
utilizing an unsupervised K-means clustering. The proposed
method empowers compelling fragment of the hued picture
iteratively in light of the necessity. Utilizing this approach it is
anything but difficult to picture the outcomes and examine the
specific question in the picture.
Figure 2: Classification of Image Segmentation
Vu, H. N et al [7] proposes a technique which comprises of
three phases preprocessing which enhances differentiation of
grayscale picture, multi-level thresholding for isolating literary
area from non-printed protest, for example, illustrations,
pictures, and complex foundation, and heuristic channel,
recursive channel for content restricting in printed district.
Proposed procedure has accomplished similar outcomes
contrasted with the other understood content extraction
strategy, which demonstrate the effectiveness and favorable
circumstances if there should arise an occurrence of genuine
record picture.
Different Clustering Methods:
In order to perform image clustering, we initially need to pick
a portrayal space and after that to utilize a proper separation
measure (closeness measure), to coordinate amongst pictures
and group focuses in the chose portrayal space. The picture
grouping is then performed in an administered procedure,
utilizing human intercession or in an unsupervised procedure,
depending on the likeness between the pictures and the
different bunch focuses.
Nabeel, F.et al [9] proposes a basic, decreased multifaceted
nature and proficient picture division and combination
approach. . It improves the division procedure of hued pictures
by combination of histogram based K-means bunches in
different shading spaces. The pixels in the re-quantized
shading spaces are bunched into classes utilizing the K-means
(Euclidean Distance) method.
K-Means
K-means is one of the simplest unsupervised learning
algorithms that explain the well known clustering problem.
The method takes after a straightforward and simple approach
to characterize a given informational collection through a
specific number of groups (expect k clusters) settled from the
earlier. The primary thought is to characterize k centroids, one
for each group. These centroids ought to be set shrewdly as a
result of various area causes diverse outcome. In this way, the
better decision is to put them however much as could
reasonably be expected far from each other. The subsequent
stage is to take each guide having a place toward a given
informational index and associate it to the closest centroid. At
the point when no point is waiting, the initial step is finished
and an early group age is finished. Presently we need to refigure k new centroids as bury centers of the packs coming to
fruition due to the past progress. After we have these k new
centroids, another coupling must be done between comparable
educational list centers and the nearest new centroid. A circle
has been made. In view of this circle we may see that the k
centroids change their zone all around requested until the
point that the moment that no more changes are done. By the
day's end centroids don't move any more.
Ayech, M. W.et al [6] proposes a weighted element space and
a straightforward arbitrary examining ink-implies clustering
for THz picture division. In this paper endeavor to gauge the
normal focuses, select the pertinent highlights and their scores,
and group the watched pixels of THz pictures. Thus
accomplishing the best smallness inside groups, the best
segregation of highlights, and the best exchange off between
the clustering precision and the low computational cost.
G. C. Ngo et al [2] proposes a novel way to deal with
recognize 0.3 mm breaks in photos taken at a separation of 1
m from the surface. Separating and morphological operations
are connected to the picture to make the splits more
discernable from the foundation. We isolate break applicants
from whatever is left of the picture utilizing division.
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 5 (2018) pp. 2484-2489
© Research India Publications. http://www.ripublication.com
Exploratory outcomes exhibit the adequacy of the proposed
technique.
shadow Region division and Criminisi Algorithm for filling
the shadow area.
Fuzzy C-Means
Hierarchical clustering
The most admired algorithm in the fuzzy clustering is the
Fuzzy C-Means (FCM) algorithm [S]. Fuzzy c-means (FCM)
is a technique of clustering which allows one piece of data to
belong to two or more clusters. Our investigation
demonstrated that FCM has a noteworthy issue: a lot of
capacity necessity. Keeping in mind the end goal to defeat this
issue, we have built up an altered variant of FCM (from this
point forward called MFCM) which utilizes a recursive
strategy, instead of a clump system utilized as a part of FCM,
to refresh group focuses [6]. We connected the MFCM to the
picture pressure issue and showed that it can diminish the
capacity essentially. In this work, we build up a picture
partition calculation in light of the MFCM bunching
calculation. The MFCM (or FCM) groups each picture pixel (
test ) without the utilization of spatial requirements. To
enhance the division, we adjust the target capacity of the FCM
to incorporate spatial requirements. We make utilization of the
spatial requirements by accepting that the measurable model
of picture bunches is the Markov Arbitrary Field (MRF). The
MRF has stirred wide consideration as of late, which has
ended up being an intense displaying device in a few parts of
picture preparing.
The idea of hierarchical clustering is to develop a pecking
request speaking to the settled gathering of examples (for
picture, known as pixels) and the comparability levels at
which groupings change. We can apply the two-dimensional
informational collection to translate the operation of the
progressive grouping calculation. Given a set of N objects to
be clustered, and an N*N distance (or similarity) matrix, the
essential procedure of hierarchical clustering is this:

Start by doling out everything to a bunch, so that in
the event that you have N things, you now have N
groups, each containing only one thing. Let the
separations (similarities) between the groups the
same as the separations (likenesses) between the
things they contain.

Find the nearest (most comparative) combine of
bunches and union them into a solitary group, so now
you have one bunch less.

Calculate separations (similarities) between the new
bunch and each of the old groups.

Repeat steps 2 and 3 until all items are clustered into
a single cluster of size N. (*)
Brief Review of the Previous Work :
Brief Review of the Previous Work :
Stefan, R. A et al [8] proposes viability of various leveled
grouping procedures application and arrangement for imaging
setting in the Content-Based Image Retrieval (CBIR). The
outcome has the reason to analyze the got comes about
because of utilizing diverse progressive bunching calculations
with different information parameters and setups utilizing two
kinds of examination procedures. The points are additionally
to feature the execution upgrades.
Yu, C. et al [17] proposes a novel modified kernel fuzzy cmeans (NMKFCM) algorithm based on conventional KFCM
which incorporates the neighbor term into its objective
function. The consequences of trials performed on
manufactured and genuine restorative pictures demonstrate
that the new calculation is compelling and effective, and has
better execution in uproarious pictures.
Li, Y. et al [20] proposes anti-noise performance of FCM
algorithm. The new calculation is figured by joining the spatial
neighborhood data into the participation work for bunching.
Pixels and the earlier likelihood are utilized to shape another
enrollment work. Subsequently we evacuate the commotion
spots and misclassified pixels.
Jarrah, K. et al [26] proposes a technique for managing
adjustments of a RBF-based importance criticism organize,
implanted in programmed content based picture recovery
(CBIR) frameworks, through the guideline of unsupervised
progressive grouping. Subsequently level of limitation into the
discriminative procedures with the end goal that maximal
separation turns into a need at any given determination.
Mai, D. S. et al [11] proposes a technique for enhancing fuzzy
c-means clustering calculation by utilizing the criteria to move
the model of bunches to the normal centroids which are predecided based on tests. Results demonstrate that the proposed
calculation has enhanced the nature of groups for an issue
class of land cover change discovery.
Pandey, S. et al [14] proposes a dataset with pictures
assembled semantically. It doesn't use any foundation
information related either to the semantics of pictures or the
quantity of groups framed. For getting comes about we apply
agglomerative technique for progressive grouping calculation.
At each middle of the road step, a delegate picture is signified
a group. This picture remains for each other picture having a
place with a group and subsequently there is some loss of data.
This misfortune is followed to get the quantity of bunches
consequently.
T. S. et at [1] proposes C-Means and Criminisi Algorithm
Based Shadow evacuation conspire for the Side Scan Sonar
Images like submerged sea examinations like mining,
pipelining, protest identification, submerged interchanges and
so on. Therefore we can get clear perspective of distinguished
question utilizing Fuzzy c-means clustering calculation for
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Yang, T. et al [22] proposes an original thought of this file
structure which called CBC-Tree. The grouping data is spared
to the file record and furthermore B+ tree is utilized to recover
the last level of CBC-Tree. We joined the recovery capacity of
B+ tree and CBC-Tree together; the proficiency of picture
recovery is made strides. Our examination comes about
demonstrate that the enhanced ordering structure is proficient.
Peter Orbanz et al [26] proposes an issue of picture division
by clustering nearby histograms with parametric blend ofblend models. These models speak to each group by a solitary
blend model of straightforward parametric parts, normally
truncated Gaussians. Results are exhibited for utilization of the
calculation to unsupervised division of synthetic aperture radar
(SAR) pictures.
Inoue, K.et al [20] proposes a strategy for expelling lines and
sections in a picture by grouping the arrangements of lines and
segments independently. We additionally proposes a strategy
for adding lines and segments to a picture based on the
proposed push/segment expulsion technique. Test comes about
demonstrate that the proposed techniques can diminish or
develop the sizes of given pictures, while the angle
proportions of the fundamental protests in the pictures are
protected.
Li, B. et al [12] proposes Mixture of Gaussian Regression
(MoG Regression) for subspace bunching by demonstrating
clamor as a Mixture of Gaussians (MoG). The MoG
Regression gives a successful method to demonstrate a
considerably more extensive scope of commotion dispersions.
Thus, the acquired proclivity grid is better at portraying the
structure of information in genuine applications. Trial comes
about on various datasets exhibit that MoG Regression
fundamentally beats cutting edge Sub space clustering
techniques.
Senthilnath, J. et al [15] proposes hierarchical clustering
algorithms for arrive cover mapping issue utilizing multighastly satellite pictures. In unsupervised procedures, the
programmed age of number of bunches and its places for a
colossal database aren’t abused to their maximum capacity.
ANN Clustering
The Artificial Neural Network (ANN) approach to clustering
has strong theoretical links with actual brain processing. The
need is to make it more powerful and versatile in substantial
databases because of long preparing circumstances and the
complexities of complex information.
We watch that however computationally GSO is slower than
MSC, the previous calculation gives much better
characterization comes about.
Mixture of Gaussians
Brief Review of the Previous Work :
Zhang, X. Z. X.et al [25] proposes a quick shading picture
division calculation which might be utilized for vision
applications. This approach depends on Fast Learning
Artificial Neural Networks (FLANN) grouping and division in
light of rationality between neighboring pixels. The reason for
this module is to pick up an underlying general impression of
the earth and feature locales of intrigue that the perceptual
framework may worry about.
Ma, J. et al [24] proposes a novel calculation in light of
bunching to remove rules from simulated neural systems. After
systems Beijing prepared and pruned effectively, inward
principles are created by discrete enactment estimations of
shrouded units. This division calculation can be utilized to
develop an underlying visual guide of fascinating areas in the
scene. Tests have demonstrated that the visual guide produced
gives valuable starting pursuit space to objects.
Each cluster can be scientifically spoken to by a parametric
dispersion, similar to a Gaussian (nonstop) or a Poisson
(discrete). The whole informational collection is along these
lines displayed by a blend of these dispersions. An individual
circulation used to demonstrate a particular group is regularly
alluded to as a segment dispersion.
Brief Review of the Previous Work :
Suhr, J. K. et al [18] proposes a background subtraction
strategy for Bayer-design picture successions. The proposed
strategy models the foundation in a Bayer-design area utilizing
a mixture of Gaussians (MoG) and arranges the forefront in an
added red, green, and blue (RGB) space. This strategy can
accomplish nearly an indistinguishable precision from MoG
utilizing RGB shading pictures while keeping up
computational assets (time and memory) like MoG utilizing
grayscale pictures.
Sammouda, R. et al [14] proposes the genuine honey bee
scavenge territories with particular qualities like populace
thickness, environmental dissemination, and blossoming
phenology in light of shading satellite picture division.
Satellite pictures are at present utilized as an effective device
for horticultural administration and observing. The outcome
got will enable beekeepers to be all around guided about the
real honey bee to rummage zone with particular attributes like
populace thickness, biological dissemination and blooming
phenology.
Portilla, J. et al [27] proposes a strategy for expelling
commotion from computerized pictures, in view of a
measurable model of the coefficients of an over entire multiscale situated premise. Depict Neighborhoods of coefficients
at nearby positions and scales are demonstrated as the result of
two free irregular factors: a Gaussian vector and a shrouded
positive scalar multiplier. We exhibit through recreations with
pictures tainted by added substance white Gaussian
commotion that the execution of this technique generously
outperforms that of beforehand distributed strategies, both
outwardly and as far as mean squared blunder.
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COMPARATIVE STUDY AMONG
CLUSTERING METHODS:
imperatives to pick a productive grouping procedure. Time to
time adjustments done in the current systems of grouping may
bring better outcomes according to the prerequisite of the
handling model.
DIFFERENT
Here we compare different clustering methods on basis of
some parameters given below:

Algorithm works on

Data Center

Best result Criteria

Disadvantages

Advantages
REFERENCES
[1]
Dsce, T. S. (2017). Algorithm Based Shadow Removal
Scheme for Side Scan Sonar Images, (Icimia), 411–
414.
[2]
Noh, Y., Koo, D., Kang, Y., Park, D., & Lee, D.
(2017). Automatic Crack Detection on Concrete Images
Using Segmentation via Fuzzy C-means Clustering,
877–880.
[3]
Yadav, H., Bansal, P., & Kumarsunkaria, R. (2016).
Color dependent K-means clustering for color image
segmentation of colored medical images. Proceedings
on 2015 1st International Conference on Next
Generation Computing Technologies, NGCT 2015,
(September),
858–862.
https://doi.org/10.1109/NGCT.2015.7375241
[4]
Pillai, B., Mounika, M., Rao, P. J., & Sriram, P. (2016).
Image Steganography Method Using K-Means
Clustering and Encryption Techniques. Intl. Conference
on Advances in Computing, Communications and
Informatics (ICACCI), 1206–1211.
[5]
G. C. Ngo and E. Q. B. Macabebe, “Image
Segmentation Using K-Means Color Quantization and
Density-Based Spatial Clustering of Applications with
Noise ( DBSCAN ) for Hotspot Detection in
Photovoltaic Modules,” pp. 1617–1621, 2016.
[6]
Ayech, M. W., & Ziou, D. (2015). Automated feature
weighting and random pixel sampling in k-means
clustering for terahertz image segmentation. IEEE
Computer Society Conference on Computer Vision and
Pattern Recognition Workshops, 2015–October, 35–40.
https://doi.org/10.1109/CVPRW.2015.7301294
[7]
Vu, H. N., Tran, T. A., Na, I. S., & Kim, S. H. (2015).
Automatic extraction of text regions from document
images by multilevel thresholding and k-means
clustering. 2015 IEEE/ACIS 14th International
Conference on Computer and Information Science,
ICIS
2015
Proceedings,
329–334.
https://doi.org/10.1109/ICIS.2015.7166615
[8]
Stefan, R. A., Szöke, I. A., & Holban, S. (2015).
Hierarchical clustering techniques and classification
applied in Content Based Image Retrieval (CBIR).
2015 IEEE 10th Jubilee International Symposium on
Applied Computational Intelligence and Informatics,
147–152. https://doi.org/10.1109/SACI.2015.7208188
[9]
Nabeel, F., Asghar, S. N., & Bashir, S. (2015).
Optimizing image segmentation by selective fusion of
histogram based K-means clustering. Proceedings of
2015 12th International Bhurban Conference on
CONCLUSION
To the extent the different talked about strategies are
concerned, we comprehend to a bound level that each of the
grouping strategy has its own System and process for
performing distinctive assignment. K-implies grouping can be
utilized as an effective system, works great notwithstanding
for expansive measured informational collections however it
might prompt generation of wasteful outcomes in nearness of
clamor. This can be overwhelmed by the utilization of fluffy
C-implies bunching and some more. Be that as it may, the
strategy for grouping to be received ought to be dealt with as it
might prompt over and under division. Consequently
everything relies upon the picture handling model and its
2488
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 5 (2018) pp. 2484-2489
© Research India Publications. http://www.ripublication.com
Applied Sciences and Technology, IBCAST 2015, 181–
185. https://doi.org/10.1109/IBCAST.2015.7058501
[10]
[11]
[12]
[13]
[14]
[15]
Sherbrooke, D. (2015). RANKED K-MEANS
CLUSTERING
FOR
TERAHERTZ
IMAGE
SEGMENTATION Mohamed Walid Ayech Djemel
Ziou D ´ epartement d ’ informatique , Universit ´ ebec ,
Canada. International Conference on Image Processing
(ICIP), 4391–4395.
Mai, D. S., & Ngo, L. T. (2015). Semi-supervised fuzzy
C-means clustering for change detection from
multispectral satellite image. IEEE International
Conference on Fuzzy Systems, 2015–Novem.
https://doi.org/10.1109/FUZZ-IEEE.2015.7337978
Li, B., Zhang, Y., Lin, Z., & Lu, H. (2015). Subspace
clustering by Mixture of Gaussian Regression.
Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, 7-12NaN-2015,
2094–2102.
https://doi.org/10.1109/CVPR.2015.7298821
Mathur, N., Dadheech, P., & Gupta, M. K. (2015). The
K-means Clustering Based Fuzzy Edge Detection
Technique on MRI Images. 2015 Fifth International
Conference on Advances in Computing and
Communications
(ICACC),
330–333.
https://doi.org/10.1109/ICACC.2015.103
Pandey, S., & Khanna, P. Sammouda, R., Touir, A.,
Reyad, Y. A., Adgaba, N., Ai-Ghamdi, A., & Hegazy,
S. S. (2013). Adapting artificial hopfield neural
network for agriculture satellite image segmentation.
International Conference on Computer Applications
Technology,
ICCAT
2013.
https://doi.org/10.1109/ICCAT.2013.6521962
Senthilnath, J., Omkar, S. N., Mani, V., Tejovanth, N.,
Diwakar, P. G., & Shenoy B, A. (2012). Hierarchical
Clustering Algorithm for Land Cover Mapping Using
Satellite Images. Selected Topics in Applied Earth
Observations and Remote Sensing, IEEE Journal of,
5(3),
762–768.
https://doi.org/10.1109/jstars.2012.2187432
[16]
Serikawa, S. (2012). Robust color image segmentation
method based on weighting Fuzzy C-Means Clustering.
2012 IEEE/SICE International Symposium on System
Integration
(SII),
133–137.
https://doi.org/10.1109/SII.2012.6426961
[17]
Yu, C., Li, Y., Liu, A., & Liu, J. (2011). A Novel
Modified Kernel Fuzzy C-Means Clustering Algorithm
on
Image.
Ieee,
621–626.
https://doi.org/10.1109/CSE.2011.231
[18]
Suhr, J. K., Jung, H. G., Li, G., & Kim, J. (2011).
Mixture of gaussians-based background subtraction for
bayer-pattern image sequences. IEEE Transactions on
Circuits and Systems for Video Technology, 21(3),
365–370.
https://doi.org/10.1109/TCSVT.2010.2087810
2489
[19]
Li, Y., & Shen, Y. (2010). Fuzzy c-means clustering
based on spatial neighborhood information for image
segmentation. Journal of Systems Engineering and
Electronics,
21(2),
323–328.
https://doi.org/10.3969/j.issn.1004-4132.2010.02.024
[20]
Inoue, K., Hara, K., & Urahama, K. (2010). Image
resizing by row/column removal and addition based on
hierarchical clustering. IEEE Region 10 Annual
International Conference, Proceedings/TENCON, (2),
1673–1678.
https://doi.org/10.1109/TENCON.2010.5686034
[21]
Thomas Kinsman + , Peter Bajorski †, Jeff B . Pelz + ,
IEEE Members † Center for Quality and Applied
Statistics and Carlson Center for Imaging Science ,
Rochester Institute of Technology. (2010), 58–61.
[22]
Yang, T., & Xu, H. (2009). An Image Index Algorithm
Based on Hierarchical Clustering. 2009 Fifth
International Conference on Intelligent Information
Hiding and Multimedia Signal Processing, 459–462.
https://doi.org/10.1109/IIH-MSP.2009.191
[23]
Ma, J., Guo, D., Liu, M., Ma, Y., & Chen, S. (2009).
Rules extraction from ANN based on clustering.
Proceedings of the 2009 International Conference on
Computational Intelligence and Natural Computing,
CINC
2009,
(2),
19–21.
https://doi.org/10.1109/CINC.2009.168
[24]
Zhang, X. Z. X., & Tay, A. L. P. (2007). Fast Learning
Artificial Neural Network (FLANN) Based Color
Image Segmentation in R-G-B-S-V Cluster Space. 2007
International Joint Conference on Neural Networks, 1–
6. https://doi.org/10.1109/IJCNN.2007.4371018
[25]
Jarrah, K., & Krishnan, S. (2006). Automatic ContentBased Image Retrieval Using Hierarchical Clustering
Algorithms. The 2006 IEEE International Joint
Conference on Neural Network Proceedings, 3532–
3537. https://doi.org/10.1109/IJCNN.2006.247361
[26]
Sar Images As Mixtures Of Gaussian Mixtures Peter
Orbanz and Joachim M . Buhmann. (2005), 0–3.
[27]
Portilla, J., Strela, V., Wainwright, M. J., & Simoncelli,
E. P. (2003). Image denoising using scale mixtures of
Gaussians in the wavelet domain. IEEE Transactions
on
Image
Processing,
12(11),
1338–1351.
https://doi.org/10.1109/TIP.2003.818640