Abstract: factors of an image. Gray Level Co-occurrence

Abstract: In the domain of Image processing, Image
mining is advancement in the field of data mining. Image mining is the extraction of hidden data, association of image data and
additional pattern which are quite not clearlyvisible in image. It’s an
interrelated field that involves, Image Processing, Data Mining, Machine
Learning, Artificial Intelligence and
Database. The lucrative point of Image Mining is that without any prior
information of the patterns it can generate all the significant patterns. This is the writing for a research done on
the assorted image mining and data mining techniques. Data mining refers to the extracting of knowledge/information
from a huge database which is stored in further multiple heterogeneous databases. Knowledge/information is
communicating of message through direct or indirect technique. These techniques include neural network, clustering,
correlation and association. This writing gives an introductory review
on the application fields of data mining which is varied into
telecommunication, manufacturing, fraud
detection, and marketing and education sector. In this technique we use size,
texture and dominant colour factors of an image. Gray Level Co-occurrence
Matrix (GLCM) feature is used to determine the texture of an image. Features such as texture and color are normalized. The image
retrieval feature will be very sharp using the texture and color feature of image attached with the shape feature.
For similar types of image shape and texture feature, weighted Euclidean
distance of color feature is utilized for retrieving features.

 

Keywords: Data Mining, Feature Extraction, Image Retrieval, knowledge discovery
database.

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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
  shows how 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 5 8.

 

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. A survey paper www.iosrjournals.org20 | Page shows the popularity of the education institutes. We use
students data to analyse their learning behaviours to predict the desired
results 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 14.

 

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.

 

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