AMALGAMATION in any college or university and discover the

AMALGAMATION OF DATA MINING
AND IMAGE PROCESSING TECHNIQUES IN IMAGE RETRIEVAL

 

K. Kamala Kannan1,
Dr. G. Anandharaj2

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Assistant
Professor, PG & Research Department of Computer Science, Sun Arts and
Science College, Vettavalam Road, Keeranoor Village, Rajapalayam Post,
Tiruvannamalai-606 7551

Assistant
Professor & Head, PG & Research Department of Computer Science,
Adhiparasakthi College of Arts and Science (Autonomous),
G.B.Nagar, Kalavai – 632 506.2

 

Abstract: In the field of Image Processing, Mining is a new technique. Image
mining is the extraction of latent data, amalgamation of image data and surplus
patterns which are invisible in images. This involves Image Processing, Data
Mining etc., without any prior notice, it can create all the important patterns.
This research paper focuses on the extracting of knowledge from a huge
database. Information is transfering message through direct or indirect
technique. These techniques are neural network, clustering, correlation and
association. This writing explains how data mining is used in the fields of
telecommunication, fraud detection, manufacturing, marketing and education
sector. Through this  technique, we can
use size, texture and dominant colour factors of an image. Gray Level
Co-Occurance Matrix(GLCM) feature determines the texture of an image. Texture
and colour features are normalized. Data Mining predicts the earth quake and
Bioinformatics. It also analyses agricultural fields. This is used in cloud
computing to retrieve meaningful information from data storage.

                                  

Keywords: Data Mining, Feature Extraction, Image Retrieval, Clustering, knowledge discovery
database, Gray Level Co-Occurance Matrix and Cloud Computing,.

 

I. INTRODUCTION

DATA
MINING

 

In the actual world,
massive amount of data is found in the education, industry, medical and many
other branches. These data may give knowledge and information for decision
making. For instance, we can detect the drop out students in any college or
university and discover the sales details in the shopping databases. These data
can be analysed, shortened or understood to meet the challenges.1 Data Mining
is the significant idea for data analysis, discovering amazing patterns from the
large data, knowing the data stored in various databases, like warehouse, World
Wide Web and external sources. The pattern is to understand the unknown valid
and the potential data. Data Mining is a kind of sorting techniques used to
extract the hidden patterns. Their goals are past recovery of data or
information. They help us identify the hidden patterns and reduce the level of
complexity. They also save time.2 Data Mining is sometimes treated as Knowledge
Discovery in Database(KDD).3 KDD process consists of following steps shown
below.

 

Fig.1. Knowledge Data Mining

 

·   
Selection: Select data from various resources where operation to be
performed.

·   
Preprocessing: Also known as data cleaning in which remove the unwanted data.

·   
Transformation: Transform/consolidate into a new format for processing.

·   
Data mining: Identify the desire result.

·   
Interpretation/Evaluation: Interpret the result/query to give meaningful information.

 

Various algorithms and techniques
like Classification, Clustering, Regression, 
Artificial Intelligence,  Neural
Networks,  Association  Rules, 
Decision  Trees,  Genetic Algorithm, Nearest Neighbor method
etc., are meant for knowledge discovery
from  databases5. The main
objective of this paper learns about the data mining and the rest of this Section 2 discusses data mining
models and techniques. Section 3 explores the application of data mining.
Finally, we conclude the paper in Section 4.

 

 

IMAGE
MINING

 

This is to search and
discover the valid and hidden data largely. The above figure (Fig.1) exhibits
the different process of Image mining system. Some other methods too used to
gather knowledge. They are, Image Retrieval, Data Mining, Image Processing and
Artificial Intelligence. The methods permit Image mining to follow two
different approaches. The first is to extract from databases or images. The
second is to mine the alphanumeric data or images. Here the feature extraction
reduces dimensionally. If the input data is more to be accessed, it is doubted
as notoriously repeated, then the input data will be changed into a reduced set
of features. It simplifies the quantity of resources needed to locate a lot of
data clearly. Many other features are used in Image Retrieval system. The most
famous are color features, shape features and texture ones.

 

Fig.2. Image Mining Process

 

II. FEATURE EXTRACTION

 

                Generally the feature extraction has got a major
problem in detecting the objects but the Genetic Algorithm (GA) gives an easy
common and powerful framework for detecting the better sets of features.
Therefore it leads to lower detection error rates. Zehange Sun et al., 13
debate to carry out the method using Principle Component Analysis (PCA) and
Classifications using Support Vector Machines (SVMs). Hence GA can remove the
detection and unwanted features. The methods have two difficult objects
detection problems – vehicle detection and face detection. They boost the
performance of both systems using SVMs for classification. According to Patricia
G. Foschi 10, feature selection and extraction is the pre-processing step of
Image Mining. It is a critical step. Mining from images is to extract patterns
and derive the images. Its aim is to identify the best ones. In the views of
Broun, Ross A et al., 3 discuss the need of digital images forensics which
underpin the design of mining system. It can be trained by a hierarchical SVMs
to detect objects. Image mining generally deals with the study and development
of new technologies. It is not only to rediscover relevant images; but also to
innovate the image patterns. Fernandez. J et al.,4 exhibit how a natural
source of parallelism can be utilized to reduce the cost of mining. The images
from the database are first pre-processed to improve their quality. They
undergo several transformations to generate the important features from the
images with the help of generated features. Mining can be done using data
mining techniques to discover important patterns.

 

A. Color Features

 

Image Mining gives
unique characteristics due to the richness of the data shown. Its evaluation
result needs the performance parameters. Aura Conci et al.,2 refer an
evaluation for comparing the function by colour. Experiments with colour
affinity mining by quantization on colour space and measures of similarity
illustrate the scheme. Lukaz Kobylinski and Krzysztof Walczak 9 proposed a
fast and effective method of indexing image metadatabases. The index is made to
their colour characteristics Binary Thresholded Histogram (BTH), a color
feature description method to create a metadatabase index. The BTH is proved to
be a sufficient method to show the characteristics of image databases.

Ji Zhang, Wynne Hsn and
Mong Li Lee 8 recommended an effective information driven framework for image
mining. They divided four levels of information: Pixel Level, Object Level,
Semantic Concept Level and Pattern and Knowledge Level.

 

B. Texture Feature

 

The Human percepts the
images which is based on the color histogram texture. The Human Neurons hold
the 1012 of information; the Human brain knows everything with the sensory
organs like eye which transfers the image to the brain which interprets the
image. According to Rajshree S. Dubey et al., 12 the mining images are based
on the color Histogram, texture of Image. Janani. M and Dr. Manicka Chezian. R 7
refer that Image Mining is a pivotal method used to mine knowledge from Images.
This is based on the content based Image Retrieval system. Color, texture,
pattern and shape of objects are the basis of visual content.

 

C. Shape Feature

 

According to Peter
Stanchev 11 a new method is proposed on extraction of low level color, shape
and texture into high level semantic features with the help of an image mining
method. Johannes Itten’s theory is offered for getting high level shape features.
Harini D.N.D and Dr. Lalitha Bhaskari. D 5 argue that Image Retrieval is
simply to reveal out low level pixel representation to recognize high level
image objects and their relationships 22,23.

 

Fig.3.
Content Based Image Retrieval System Architecture

 

III. METHODOLOGY

 

The gray-level co-occurrence matrix (GLCM) considers
the relationship of pixels. This calculates how often the pairs of pairs of
pixel with specific values and in a specified spatial relationship in an image.

 

Understanding a Gray-Level
Co-Occurrence Matrix

 

We use the graycomatrix functionto make a GLCM. It
creates GLCM by calculating how often a pixel with the intensity (Gray Level)
value i occurs in a default. Each element (i,j) is the sum of the pixel with
value i occurred in the specified spatial relationship to a pixel with value j
in the input image. Graycomatix uses scaling to reduce the number of intensity
values. The Num levels and the Gray Limits control this scaling of gray level.
Let us understand the process through the following diagram. The following
figures explains how graycomatrix calculates the first three values in a GLCM.

To illustrate, the following figure show graycomatrix
calculates the first three values in a GLCM. In the output GLCM, element (1,1)
contains the value 1 because there is only one instance in the input image
where two horizontally adjacent pixels have the values 1 and 1, respectively.
glcm(1,2) contains the value 2 because there are two   instances where two horizontally  adjacent 
pixels  have  the 
values 1 and 2. Element (1,3) in the GLCM has the value 0 because there
are no instances of two horizontally adjacent pixels with the values  and 3.graycomatrix continues processing the
input image, scanning the image for other pixel pairs (i,j) and recording the
sums in the corresponding elements of the GLCM.

 

Fig.4. Process used to create the GLCM

 

Specify Offset Used in GLCM Calculation

 

By default, a single GLCM is created by the
graycomatrix with offset as two horizontally adjacent pixels. A single GLCM
might not adequate to describe the texture features of the input image. A
single offset might not be sensitive to texture. So graycomatrix can make
multiple GLCM for a one input image. The offsets produce multiple GLCM to
graycomatrix function. They define mainly pixel relationships of different
directions (Horizontal, Vertical and Two diagonals) and four distances. In this
way, the input image is shown by 16 GLCMs. When we calculate statistics from
these GLCMs, we can take the average.

 

Weighted Euclidean Distance

 

The standardized Euclidean distance between two
J-dimensional vectors can be written as:

 

 

Where sj is the sample
standard deviation of the j-thvariable. Notice that we need not subtract the j-th mean from xj
and yj because they will just cancel out in the differencing. Now
(1.1) can be rewritten in the following equivalent way:

 

 

Where wj = 1/sj2is the inverse of
the j-th variance. wj as a weight attached to the j-th variable: in
other words

 

IV. DATA MINING TECHNIQUES

 

Data mining is gathering relevant information from
disordered data. So it helps achieve specific objectives. Its aim is simply
either to create a descriptive model or a predictive model. A description
refers the main characteristics and a prediction allows the data miner to
predict an unknown (often future) values of specific or the target variable.7
Simply their goals are to use a variety of data mining techniques as shown in
the figure 58.

 

Fig.5. Data Mining Models

 

3.1 Classification: It is based on discrete and
unordered. This is based on the desired output. It classifies the data based on
the training set and values. These goals achieve using a decision tree, neural
network and classification rule(If-Then). For instance we can apply this rule
on the past record of the students who left for university and evaluate them.
This helps us identify the performance of the students.

 

3.2 Regression: It is utilized to map a data
part to a real valued prediction variable. 8 It can be used for prediction
too. Here, the target values are known, for example, we can predict the child
behavior based on family history.

 

3.3 Time Series Analysis: This process used the statistical techniques to model. It
explains a time dependent series of data points. It is a method of using a
model to create prediction (forecasts) for future happenings based on known
past events.9 Stock market is a good example.

 

3.4 Prediction: This techniques discover the relationship between independent
variables and dependent and independent variable.4 This model is based on
continuous or organized value.

3.5 Clustering: It is a gathering of some data objects. Another cluster is
dissimilar object. It generally finds out the similarities between the data of
the same qualities. This is based on the unsupervised learning. For instance,
city planning, image processing, pattern recognition, etc.,

 

3.6 Summarization: This is abstraction of data. It
is formed as a set of related task. It provides an overview of data. For example,
running race for long distance can be shortened total minutes or seconds.
Association rule is another famous technique to mine the data. It find the most
frequent item set. It discovers the patterns in data of relationships between
items in the same transaction. It is also referred as “relation technique” as
it relates the sets/items.6

 

3.7 Sequence Discovery: This sequence discloses the relationships among data.8 It is a
set of object associated with its own timeline of events. Natural disaster and
analysis of DNA sequence and scientific experiments are best examples.

 

V. DATA MINING APPLICATIONS

 

Data mining is applied for fast
access of data and valid information from a huge amount of data. Its main area
includes marketing, fraud detection, finance, telecommunication, education
sector, medical field, etc., some of the main applications are categorized
below.

 

4.1 Data Mining in Education Sector: This is used in new emerging field called “Education
Data Mining”. This helps know performance of students, dropouts, students’
behaviour and their choice of different courses. It is highly used in higher education
sector.10

 

4.2 Data Mining in Banking and Finance: It is used largely in the Banking and Financial market.11
It mines the credit card fraud, estimate risk and trend and profitability. In
financial markets, it plays as a neural networks in stock forecasting price
prediction etc.,

 

 

 

4.3 Data Mining in Market Basket Analysis:  These
methodologies are based on the shopping database. Their goal is to find out the
products and the customers purchase. The shopping can utilize this information
by putting these products more visible and accessible for customers.12

 

4.4 Data Mining in Earthquake Prediction: This predicts the earthquake from the satellite maps.
The quake is the sudden movement of the Earth’s crust caused by the abrupt
release of stress of a geologic fault in the interior. This is done in two
types of prediction: forecasts (months to years in advance) and short-term
predictions (hours or days in advance) 13.

 

4.5 Data Mining in Bioinformatics: Bioinformatics created a huge amount of biological
data. This is a new field of inquiry to generate and integrate large quantities
of proteomic, genomic and other data.4

 

4.6 Data Mining in Telecommunication: This field has large amount of data consisting of huge
customers. So it is need to mine the data to limit the fraudulent, improve
their marketing efforts and better management of networks.4

 

4.7 Data Mining in Agriculture:  This is mainly used to
produce more crop yields. This is done in four parameters namely year,
rainfall, production and area of sowing. It is used to improve yield from the
prediction data. It can be promoted by using data mining techniques such as K
Means, K nearest neighbor (KNN), Artificial Neural Network and support vector
machine (SVM) 14,20.

 

4.8 Data Mining in Cloud Computing:  This technique
is used in cloud computing. Through cloud computing, the mining technique will
permit the users to retrieve correct information from the data warehouse. This
lessens the cost of infrastructure and storage.15, 21 It utilizes the
internet services to relay on clouds of servers to manage tasks. This
techniques in cloud computing performs efficient, reliable and secure services
for their users.

 

VI. CONCLUSION

 

This article presents the expansion of Image mining. It
gives a research on the image techniques measured earlier. This review finds on
challenges and accountability of different prospects. This mainly focuses on
data mining techniques in various projects. Its aim is to get information by
current data. People from different fields can utilize association, clustering,
prediction and classification techniques.

 

REFERENCES

 

1.   Janani M and Dr. ManickaChezian. R, “A Survey On Content Based
Image Retrieval System”, International Journal of Advanced Research in Computer Engineering & Technology,
Volume 1, Issue 5, pp 266, July 2012.

 

2.   Aboli W.
Hole Prabhakar L. Ramteke, “Design and Implementation of Content Based Image Retrieval Using Data Mining
and Image Processing Techniques”
International Journal of Advance Research in Computer Science and
Management Studies Volume 3, Issue 3, March 2015 pg. 219-224

 

3.   Anil K. Jain and AdityaVailaya, “Image
Retrieval using color and shape”, In Second
Asian Conference on Computer Vision, pp 5-8. 1995.

 

4.   Harini. D. N. D and Dr. LalithaBhaskari. D,
“Image Mining Issues and Methods Related to
Image Retrieval System”, International Journal of Advanced Research in
Computer Science, Volume 2, No. 4, 2011.

 

5.   Hiremath.  P. 
S  and  JagadeeshPujari,  “Content  Based 
Image Retrieval based on Color, Texture and Shape features using Image
and its complement”, International Journal of Computer Science and Security,
Volume (1) : Issue (4).

 

6.   Brown, Ross A., Pham, Binh L., and De Vel,
Olivier Y, “Design of a Digital Forensics
Image Mining System”, in Knowledge Based Intelligent  Information 
and  Engineering  Systems, 
pp  395-404, Springer Berlin Heidelberg, 2005.

 

7.   Rajshree S. Dubey, NiketBhargava and
RajnishChoubey, “Image Mining   using  
Content   Based   Image  
Retrieval   System”, International
Journal on Computer Science and Engineering, Vol. 02, No. 07, 2353-2356, 2010.

 

8.   Aura Conci, Everest Mathias M. M. Castro, “Image mining by
Color  Content”,  In  Proceedings  of  2001 
ACM  International Conference
on Software Engineering and Knowledge Engineering (SEKE), Buenos Aires,
Argentina Jun 13-15, 2001.

 

9.   Er. RimmyChuchra “Use of Data Mining Techniques for the Evaluation of Student Performance: A Case Study” International Journal of
Computer Science and Management Research Vol. 01, Issue 03 October 2012.

 

10. Ji Zhang, Wynne Hsu
and Mong Li Lee, “An Information-Driven Framework  for 
Image  Mining”  Database  and 
Expert  Systems Applications in
Computer Science, pp 232 – 242, Springer Berlin Heidelberg, 2001.

 

 

 

 

 

11. LiorRokach and
OdedMaimon, “Data Mining with Decision Trees: Theory and Applications (Series
in Machine Perception andArtificial  Intelligence)”,  ISBN:  981-2771-719,
World Scientific Publishing Company, 2008.

 

12.  Venkatadri.M and Lokanatha C. Reddy ,”A
comparative study on decision tree
classification algorithm in data mining” , International Journal Of
Computer Applications In Engineering ,Technology And Sciences (IJCAETS), Vol.-
2 ,no.- 2 , pp. 24- 29 , Sept 2010.

 

13.  XingquanZhu, Ian Davidson, “Knowledge Discovery and Data Mining:  Challenges  and 
Realities”,  ISBN 978-1-59904-252, Hershey, New York, 2007.

 

14. Zhao,
Kaidiand Liu, Bing, Tirpark, Thomas M. and Weimin, Xiao,              “A Visual Data Mining
Framework for Convenient Identification of Useful
Knowledge”, ICDM ’05 Proceedings of the Fifth IEEE International Conference on Data Mining, vol.-1, no.-1,pp.-
530-537,Dec 2005.

 

15. Li 
Lin,  Longbing  Cao,  Jiaqi 
Wang,  Chengqi  Zhang, “The Applications of Genetic
Algorithms in Stock Market Data Mining Optimisation”,
Proceedings of Fifth International Conference on Data Mining, Text Mining and their Business Applications, pp-593-604,sept
2005.

 

16.  V.
Gudivada and V. Raghavan. Content-based image retrieval systems. IEEE
Computer, 28(9):18-22, September 1995.

 

17. J.
Han and M. Kamber. “Data Mining, Concepts and Techniques”, Morgan Kaufmann, 2000.

 

18. Nikita Jain, Vishal
Srivastava “DATA MINING TECHNIQUES: A SURVEY
PAPER” IJRET: International Journal of Research in Engineering and
Technology, Volume: 02 Issue: 11 | Nov-2013.

 

19. Peter   Stanchev, “Image  
Mining   for   Image  
Retrieval”,   In Proceedings of the IASTED Conference on Computer
Science and Technology, pp 214-218, 2003.

 

20. Jeyakumar, Balajee, MA
SaleemDurai, and Daphne Lopez. “Case Studies in Amalgamation
of Deep Learning and Big Data.” HCI Challenges and Privacy Preservation in Big Data Security, pp 159, 2017.

 

21. Ranjith, D., J. Balajee, and C. Kumar. “In premises of cloud computing and models.” International Journal of Pharmacy and Technology 8, no. 3,
pp. 4685-4695, 2016.

 

22.  Kamalakannan,
S. “G., Balajee, J., SrinivasaRaghavan., “Superior content-based video
retrieval system according to
query image”.” International
Journal of Applied Engineering Research 10, no. 3, pp7951-7957,
2015.

 

23.  Rajeshwari,
A., T. C. Prathna, J. Balajee, N. Chandrasekaran, A. B. Mandal, and A. Mukherjee.
“Computational approach for particle size measurement of silver
nanoparticle from electron microscopic image.” Int. J. Pharm. Pharm. Sci. 5,
no. 2 pp619, 2013.