IRJET- Finding Dominant Color in the Artistic Painting using Data Mining Technique: A Survey

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 10 | Oct 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 235
Finding Dominant Color in the Artistic Painting using Data Mining
Technique: A Survey
Piyush. S. Atram1, Prof. Pramila M. Chawan2
1M.Tech Student, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India
2Associate Professor, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India
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Abstract - In this article aims to discover the color which
has more impact in a painting via opencv library with python
programming language. To be able to find dominant color I’ll
use k means clustering as opposed to try and find histogram
and other clustering methods in the data mining techniques
for every pixel. I'll use numpy and sklearn libraries for
clustering. In this article, the focus will be on improving and
implementing k means algorithm 2-3 timesfasterthanusualk
means clustering algorithmbesidesfindingthedominantcolor
in the input images.
Key Word: Data mining, k means, hierarchical, histogram,
opencv, artistic painting, clustering
1. INTRODUCTION
Painting is the task of applying paint, color, conceal or other
medium to a strong surface. Painting is a significant
structure inside the visual expressions, acquiring
components including drawing, motion (as in gestural
painting), arrangement or reflection (as in synopsis work of
art). By making the work of art digitizedithasbeenseenthat
we can recognize and control picture features. In the
painting, it is beyond the realm of imagination to expect to
recognize dominant color physically. The most dominating
color pixels are dictated by clustering the image pixels. The
dominant color of pictures can likewise be utilized in
applications outside oflooking.Systemsthatshowimageson
a PC show or TV may naturally create a matte that
encompasses each image. Such systems may choose a color
pixels for the dominant color.
Anyway crude information can't be utilized
straightforwardly. Its genuine worth is anticipated by
removing data valuable for choice help. In many zones,
information investigationwasgenerallya manual procedure.
At the point when the size of information control and
investigation goes past human capacities, individualssearch
for registering advances to computerize the procedure. Few
of them discussed below:
1.1 K Means
K mean is unsupervised learning algorithm that take care of
the clustering problem. The method defines a basic and
simple approach to group a given data points through a
specific number of clusters (let k clusters).Basically,thought
is to define k centers, one for each cluster. These centers
ought to be put in a path due to various location May causes
distinctive outcome.
Clustering is significant and fundamental idea ofdata mining
field utilized in different systems. In Clustering, data points
are partitioned onto different clusters. These clusters
represents to some significant target.Means,clustersare the
block of comparative objects.
1.2 Histogram
Histogram is considered as a diagram or plot which is
related to recurrence of pixels in a Gray Scale Image. With
pixel regards (reaching out from 0 to 255). Grayscalepicture
is a picture wherein the estimation of each pixel is an
example model, that is, it passes on just power information
where pixel worth vacillates from 0 to 255. Pictures of this
sort, generally called high differentiate, are made uniquely
out of shades of diminish, moving from dim at the weakest
power to white at the most grounded where Pixel can be
considered as an each point in an image.
1.3 Hierarchical clustering
Various hierarchical clustering includes making groups that
have a top to bottom approach. For instance, all documents
and envelopes on the hard circle are composed in a chain of
importance. There are two kinds of various hierarchical
clusters, Divisive and Agglomerative.
2. LITERATURE REVIEW
2.1 Data mining techniques
In data mining, there are a few procedureswhichareutilized
for clustering the data points. For example k means,
hierarchical, Gaussian (EM) grouping, Fuzzy C-means,
Density based clustering.
Based on exactness and running time the performance of k
means and hierarchical clustering calculation is determined
utilizing numerous different tools.
This work results that exactness of k means is higher than
the various clustering strategies. So for huge data points k
means algorithm is great.

2.2 Clustering with k means:
The generally utilized strategy in data mining is well known
as k means clustering. This is straightforward and
implementable so as to group the data points in various
clusters according to the class necessity.
K means algorithm needs to calculate the distance between
every data object and all group pointsineveryiteration. This
tedious procedure influences the productivity of clustering
algorithm. In any case, it can be improved by thinking about
different measures.
Consequently different further research expected to
concentrate on issues that debasingthealgorithm execution.
2.3 Retrieval of color from painting
The research work on the finding the color fromthepainting
which might contains the combination of multi-color may
result with the help of image processing techniques
“Kodituwakku and Selvarajah, (2004) investigated the
retrieval efficiency of color histograms, color moments and
color coherent vectors (CCV) by means of precision and
recall.”
In the recent work, open source library for imageprocessing
is implemented to generate the features accurately from the
digital image. The authors concluded that combination of
color descriptors producedbetterretrieval ratecompared to
individual color descriptors with the help of machine
learning techniques.
3. PROPOSED SYSTEM
3.1 Problem statement
“To find the dominant color in an artistic painting usingdata
mining technique via opencv.”
3.2 Problem Elaboration
This has been observed that finding maximum color used
from the painting image manually is bit difficult. This may
leads to excessive use of colors from pallets. But its solution
can be addressed digitally with the help of image processing
and data mining techniques. In the data mining, there are
several clustering techniques which can address the given
problem statement.
To carried out the process of finding the dominant color
from the image is done by performing the pythonscriptwith
image processing module i.e. opencv which help program to
manipulate with the image format. Furthermore the library
which is used in the proposed system like numpy and
sklearn libraries are used to perform the mathematical
modeling and clustering purposes.
Above solution will help the artists to know which color has
been used more to draw the painting.
3.3 Proposed Methodology
There are different methods in the data mining to find the
dominant color from the input image of artistic painting. But
each of them have their pros and cons which mightaffectthe
system in terms of space and time complexity. Techniques
like k means clustering, hierarchical clustering, Histogram
analysis etc.
To work efficiently with the propose system and after
understanding from the given literature survey k means
clustering technique from the data mining helps a lot as
compare to other clustering data mining techniques.
Here’s how the algorithm implemented:
1. Select K points as initial centroids.
2. Repeat this.
3. Form K cluster by assigning each point to its closest
centroids.
4. Recomputed the centroids of each cluster until means
remains unchanged.
In the above algorithm the traditional working of the k
means clustering can be implemented easily with the he k
means algorithm is known to have a time complexity of O
(n2), where n is the input data size.
In the proposed system to work with fast k means algorithm
the straightforward changes of 𝑘 means clustering strategy
to run k means faster by having few of the following steps:
1. The first stage is a fast distance calculation using only a
small set of the data to derive the best possible area of the
centers.
2. The second stage is a slow distance calculation in which
the initial centers used are taken from the first stage.
3. The fast and slow stages represent the speed of the
movement of the centers. In the slow stage, the whole data
points can be used to get the exact location of the centers
The complexity of the 𝑘 means is (𝐾𝑄𝑁) where 𝐾 is the
number of clusters, 𝑄 is the number of iteration required to
get to the stopping criteria and 𝑁 is the input.

3.4 Proposed System Architecture
The proposed system workflow is as given as
Fig -1: Work flow
As shown in above block diagram the painting image will be
taken as the input for the process and with the help of
opencv library in python to preprocess the input image and
converted into the pixels vector to be used as data sample
points for clustering with k means algorithm. By grouping
the pixels of three main different colors i.e. RGB and finding
the dominant color cluster among the present clusters with
the fast k means clustering technique discussed in the
proposed methodology.
4. CONCLUSION
In this Papers, k means clustering techniques with
corresponding method and image feature extraction are
reviewed. K meansbeingmostefficientamongdata research.
Need further improvementindifferentpartofthatclustering
algorithm. Above examined writingstudy,proposedk means
algorithm is proficiently utilizedforclusteringtheindividual
colors. This clustering algorithm can be improved with the
proposed system and can able to find the dominant color
from the artistic painting.
REFERENCES
[1] Gaurav Shrivastav and Piyush Singh. International
Journal of Engineering Research & Technology (IJERT),
“A Review: Fast Image Retrieval Based on Dominant
Color Feature”, Volume 03, Issue 01 (January 2014).
[2] Tapas Kanungo, Nathan S. Netanyahu, Angela Y. Wu.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND
MACHINE INTELLIGENCE, “An Efficient k Means
Clustering Algorithm: Analysis and Implementation”,
VOL. 24, NO. 7, JULY 2002.
[3] Serkan Kiranyaz, Stefan Uhlmann, and Moncef Gabbouj.
Department of Signal Processing, TampereUniversity of
Technology, Tampere, Finland. 2009 Seventh
International Workshop on Content-Based Multimedia
indexing, “Dominant Color Extraction basedonDynamic
Clustering by Multi-Dimensional Particle Swarm
Optimization”.
[4] E. A. Khadem, E. F. Nezhad, M. Sharifi, “Data Mining:
Methods & Utilities”, Researcher2013; 5(12):47-59.
(ISSN: 1553-9865).
[5] K. A. Abdul Nazeer, M. P. Sebastian,îImproving the
Accuracy and Efficiency of the k means Clustering
Algorithm, Proceedings of the World Congress on
Engineering 2009 Vol I WCE 2009, July 1 - 3, 2009,
London, U.K .
[6] Arhter, D. and Vassilvitskii, S.: How Slow is the kMeans
Method? SCG’06, Sedona, Arizona, USA. (2006).
[7] Elkan, C.: Using the Triangle Inequality to AccelerateK –
Means. Proceedings of the Twentieth International
Conference on Machine Learning (ICML-2003),
Washington DC, (2003).
[8] http://www.aishack.in/tutorials/kmeans-clustering/
[9] https://zeevgilovitz.com/detecting-dominant-colours-
in-python
[10] https://matthewragan.com/2018/05/17/touchdesigne
r-finding-dominant-color/

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