Abstract—Object recognition process is used in many areas for

different purposes, notably for systems performing intelligent data

analysis for automated systems. In this process, first of all, image

processing-based features of the object are extracted. Then, by

considering these features, class labeling is performed according to

the class features determined with controlled learning. The

purpose of this process is to conduct Extreme Learning Machine

(ELM)-based classification of 4 image features of the herb iris

found in UC Irvine Machine Learning Repository database. To

assess results, a comparison was made with other machine

learning methods (SVM, NB) and ELM was detected to have the

highest achievement of 99.33%.

Keywords—Artificial intelligence, Extreme Learning Machine

(ELM), Object attributes and classification.

I. INTRODUCTION

Since digital imaging has become a fundamental part of many

studies, computer-aided assessment by using advanced imaging

analysis is now an inseparable part of many research projects.

As a scientific research process, model recognition examines

and models data for the identification of a system design.

Recognition system was developed in tandem with computer

vision as a result of an extensive accumulation of knowledge.

The purpose of image processing by computers is to enable

multiple perceptions with a wide range of categories under

various conditions and to increase computer control and data

accuracy [1-5].

Object recognition is the identification of figures and objects

that have common characteristics and can be associated with

each other by using the identified features. A group of figures

and objects with common characteristics is called an object

class. Two important goals of object recognition are to:

• Identify an object belonging to a known class.

• Classify objects with unknown features.

Object recognition systems are generally made up of two

parts. In the first part, features required for object recognition or

sizes to be measured are selected. This process is called feature

extraction and the system performing this process is called a

feature extractor. Manuscript received Feb. 26, 2018 Phd. Yasin Sönmez. is now with the

Dicle University, Diyarbakır, Turkey (e-mail: yasin.sonmez@dicle.edu.tr ).

Dr. Türker Tuncer. is now with the Fırat University, Elazığ, Turkey (e-mail:

turkertuncer@firat.edu.tr ).

Prof. Dr. Engin Avcı is now with Fırat University, Elazığ, Turkey. (e-mail:

engin.avci@firat.edu.tr).

A feature is the actual number that represents the

measurement result of each feature used in the recognition of

figures or objects or size to be measured. The primary factor

affecting the performance of classification is that all features

selected must represent the objects thoroughly.

In the second part of object recognition, objects are classified

with the use of obtained feature vectors. The block diagram of

an object recognition system is given in Figure I [2].

FEATURE EXTRACTION

CLASSIFICATION

LEARNING

OBJECT REPOSITORY

IDENTIFICATION

Fig. 1. Block diagram of an object recognition system.

TABLE I: FEATURES OF IRIS

Input Output

Feature Class

Sepal length 1 Iris Setosa

2 Iris Versicolour

3 Iris Virginica

Sepal width

Petal length

Petal width

Table 2 shows the three types of the herb iris, A-setosa,

B-versicolor and C-virginica, according to their sepal and petal

feature data

Fig. 2. The kinds of iris plant A-setosa, B-versicolor ve C-virginica.

II. MATERIAL AND METHOD

Procedural steps for solving the classification problem

presented is as follows:

A. Identification of the problem

This study attempts to solve the problem as to how phishing

analysis data will be classified.

B. Data set

Approximately 150 data containing the 4 features extracted

based on the features of iris image analysis in UC Irvine

Machine Learning Repository database.

Object Classification via Extreme Learning Machine

Yasin Sönmez, Türker Tuncer, and Engin Avcı

International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 6 Issue 1 (2018) ISSN 2320-4028 (Online)

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C. Modeling

After the data is ready to be processed, modeling process

for the learning algorithm is initiated. The model is basically the

construction of the need for output identified in accordance with

the task qualifications.

D. Classification

Classification is to determine the class to which each data

sample of the methods belongs, which methods are used when

the outputs of input data are qualitative. The purpose is to divide

the whole problem space into a certain number of classes. A

wide range of classification methods are present. This is due to

the fact that different classification methods have been

constructed for different data as there is no perfect method that

works on every data set. As mentioned in literature studies, the

aim of classification is to assign the new samples to classes by

using the pre-labeled samples. The most commonly used

classification methods are described below.

• Artificial Neural Networks (ANN)

• Support Vector Machine (SVM)

• Naive Bayes (NB)

E. Extreme Learning Machine (ELM)

Extreme Learning Machine (ELM) is a feed-forward

artificial neural network (ANN) model with a single hidden

layer. For the ANN to ensure a high-performing learning,

parameters such as threshold value, weight and activation

function must have the appropriate values for the data system to

be modeled. In gradient-based learning approaches, all of these

parameters are changed iteratively for appropriate values. Thus,

they may be slow and produce low-performing results due to the

likelihood of getting stuck in local minima. In ELM Learning

Processes, differently from ANN that renews its parameters as

gradient-based, input weights are randomly selected while

output weights are analytically calculated. As an analytical

learning process substantially reduces both the solution time

and the likelihood of error value getting stuck in local minima, it

increases the performance ratio. In order to activate the cells in

the hidden layer of ELM, a linear function as well as non-linear

(sigmoid, sinus, Gaussian), non-derivable or discrete activation

functions can be used [8-15].

ELM structure is given in Figure 3.

• Artificial Neural Networks (ANN)

• Support Vector Machine (SVM)

• Naive Bayes (NB)

INPUT LAYER HIDDEN LAYER OUTPUT LAYER

i=1,2, .n j=1,2, .m k=1,2, .p

y(p)

X (1)

X (2)

X(n-1)

X(n)

.

.

b(m-1)

b (m)

b(1)

b (2)

Fig. 3. An artificial neural network model with a single hidden layer

with forward feed.

(1)

In equation 1, xi refers to input vector and yp refers to output

vector (m and n neuron count) , wi,j indicates input layer to

hidden layer weights and βj indicates output layer to hidden

layer weights, bj represents the threshold value of neurons in the

hidden layer and g(.) represents activation function. Input layer

weights (w) and bias (bj) values in the equation are randomly

assigned. Activation function (g(.)), input layer neuron count (n)

and hidden layer neuron count (m) are assigned in the beginning

step [8-15].

F. Model performance evaluation

The topics addressed in this section are the two measures that

affect the performance of the model and the algorithm used, the

first one being the division of data set into training and test data

set and the second one being the definition of expressions

measuring the performance. In the first measure, the data set is

divided into three parts as training, validation and test data by

three-phase division in K-Fold method, and model selection and

performance status are simultaneously performed. In the second

measure, performance assessment of classifier models generally

uses a validation value. Validation value can be measured as the

ratio of data count detected or estimated correctly by the

algorithm into all data in the data set.

(2)

III. EXPERIMENTAL RESULTS

100% performance was achieved in network training via

ELM and 99.33% performance was achieved in the training test

These results were obtained by using MATLAB 2103b software

and a PC with Intel i7-6500 CPU and 8 GB RAM. While

attaining these results, cell count in the hidden layer is 50 and

activation count is sigmoid for ELM.

International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 6 Issue 1 (2018) ISSN 2320-4028 (Online)

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Fig. 4. ELM performance chart.

Comparison of the results of different classification methods

Achieved performance of ELM method and achieved

performance of other machine learning methods (Support

Vector Machine (SVM), Naive Bayes (NB)) are presented in

Table 2. As deduced from these data, ELM achieved higher

performance compared to other methods in terms of

performance and speed.

TABLE I. ACCURACY OF MACHINE LEARNING METHODS.

Methods Train

Accuracy Test / True Accuracy

ELM 100% 99.33%

NB 100% 96,00%

SVM 100% 96,00%

IV. CONCLUSION AND DISCUSS

In this study, features in the database created for features

from images are classified by determining the input and output

parameters for the ELM classifier. Results obtained by ELM

show that ELM has higher achievement compared to other

classifier (SVM and NB) methods. This study is considered to

be an applicable design in automated systems with

high-performing classification against the image analyzing.

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First A. Author a Yasin Sönmez received the master

graduated in computer science from University Fırat,

Turkey in 2012. He is currently Phd student in

software engineering. end working for the computer

technology at the University of Dicle. His research

interests include Computer vision and video analysis.

yasin.sonmez@dicle.edu.tr

Türker TUNCER was born in Elazig, Turkey, in

1986. He received the B.S. degree from the Firat

University, Technical Education Faculty, Department

of Electronics and Computer Education in 2009, M.S.

degree in telecommunication science from the Firat

University in 2011 and Ph.D. degree department of

software engineering at Firat University in 2016. He

works as research assistant Digital Forensic

Engineering, Firat University. His research interests

include data hiding, image authentication, cryptanalysis, cryptography,

image processing.

Engin Avcı received the PhD degree from University of Fırat in 2000. He is

currently a professor of software engineer at Fırat University His research

interests include Computer vision and video analysis. Fuzzy Logic, Artificial

Intelligence Techniques, Biomedical, Signal Processing, Intelligent Systems,

Radar Target Recognition, Pattern Recognition, Image Processing, Data

Mining, Information Security, Cryptography

International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 6 Issue 1 (2018) ISSN 2320-4028 (Online)

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