International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
A LITERATURE SURVEY ON IMAGE CLASSIFICATION TECHNIQUES
Neel Shah1, Nalini Yadav2
1
Bachelor Of Computer Engineering, Savitrbai Phule Pune University, Pune, India, DYPIEMR,
fzr2000shahneel94@gmail.com
2
Proefssor, M.E. Computer Engineering, Savitrbai Phule Pune University, Pune, India, DYPIEMR,
yadav.nalini@gmail.com
ABSTRACT
Efficient and better techniques have always contributed to progress in the research field of Image Classification.
Image Classification is the process of sorting the picture elements into some fixed number of individual classes or
categories of data, based on features extracted from an image. Researches have shown lots of progress in image
classification but every algorithm has some of its limitation and constraints. Feature extraction and classifying
the images are two major steps in image classification. Features are data that uniquely distinguish each
individual image and they can be like size, color, texture, shape of image. Classification is process in which we
compare the input image with training data sets. This paper aims at survey of different classification techniques
in literature.
Keywords: Feature Extraction, Image Classification
----------------------------------------------------------------------------------------------------------------------------1. INTRODUCTION
Image classification is the process of classifying images according to features of individual
image. As the world is moving towards digitization, the amount of digital data i.e. digital images and
videos are increasing daily so it is very difficult to manage such a huge collection of digital images and to
organize it. Therefore we use image classification so the ease of image indexing as well as retrieval from
the huge database of digital images is done in well-ordered manner. This field is being popular since few
years but still there is scope of research. As the data increases the task of managing the huge data
increases and thus the image classification becomes difficult.
2. LITERATURE SURVEY
The process of grouping the images under relatively similar category is known as content based
image classification [1]. Features can be the content of images like size, color, shape, texture, etc. and can
be extracted from image using different image processing techniques. Classification can also be used as
preprocessing step for retrieval of image from database and also increases the rate and accuracy of
retrieving an image. Therefore major two tasks in image classification are to extract the features from the
image and are to classify the image in appropriate class label of image.
IJRISE| www.ijrise.org|editor@ijrise.org
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
Fig. 1 Image Classification Architecture
This architecture is based on supervised classification. Supervised image classification is process
of classifying the images into pre-labeled set of classes. This architecture is divided into 2 phases of
preparing training data set and validating testing data set.
a) Training Phase
Training phase contains feature extraction. Feature extraction uses many different methods like
feature extraction based on energy contents, feature extraction based on color or feature extraction based
on edges. After the features are extracted from an image, the feature vector corresponding to features are
created. After successful creation of feature vector, classify the image in appropriate class label of image.
b) Testing Phase
Testing phase also start with extracting the features from the testing image. After the features are
extracted from the new testing image, the next step is to compare the feature vector of testing image with
feature vectors of all labeled classes. Once the match is found with any of the class, the image is stored in
to that class of images and is labeled with same class.
A.
Feature Extraction
When a certain image has given to the classification system, the first step is to extract features out
of image. Actually classification includes image sensors, image pre-processing, object detection, object
segmentation, feature extraction and object classification [2]. But feature extraction is major step in image
classification. [3]Feature plays a very important role in the area of image processing so in process of
image classification also. [3]As features define the behavior of an image, they show its place in terms of
storage taken, efficiency in classification and obviously in time consumption also. Features are nothing
but every image has some unique or similar properties of like color, shape, texture or patterns etc. So all
this properties of image is called a feature of image which help them identify uniquely from others. For
each class of images, it has unique set of features. After the features are extracted, the feature vector is
created. So each class image has finally feature vector stored which are later used for classifying new set
of images. There are various techniques of feature extraction which were proposed in literature survey.
i.
Feature Extraction based on Energy Contents
Energy features of an image can be extracted with transforms. [4] A discrete cosine transform
(DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at
different frequencies. Discrete Cosine Transform (DCT) is used in many applications of digital signal
processing for information hiding, pattern recognition and content based image retrieval [5].Discrete Sine
Transform (DST) is also widely used many applications of digital signal processing as well as in image
processing [6]. Walsh functions were used as a set of normalized orthogonal functions, it is analogous to
sine and cosine functions but it has uniform values ± 1 throughout their segments [7].
IJRISE| www.ijrise.org|editor@ijrise.org
International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
ii.
Feature Extraction based on Color
Color of image can also uniquely distinguish an image from other. Color features can be extracted
from histograms. Color histograms are simple and low level techniques for image indexing and retrieval
of images [8].
[9]Support Vector Machines are supervised learning models with associated learning algorithms
that analyze data and recognize patterns, used for classification and regression analysis. [9]Given a set of
training examples, each marked for belonging to one of two categories, an SVM training algorithm builds
a model that assigns new examples into one category or the other, making it a non-probabilistic binary
linear classifier. Support Vector Machines are learning systems that use a hypothesis space of linear
functions in a hyperspace, trained with a machine learning algorithm from optimization theory that
implements a learning bias derived from statistical learning theory [10]. Interaction with image data in
terms of their features has strongly described the inherent properties of image [11]. Averaging and
Histogram techniques were used to understand and realize the color facet of an image [12, 13,14].
Block Truncation coding is an algorithm considered as a simple compression algorithm which has
primarily segmented the image into nxn non overlapping blocks [15, 16]. Static Thepade’s Ternary Block
Truncation Coding has followed Block Truncation Coding by dividing the image into Red, Green and
Blue components and treating each of the components as blocks [17]. Dynamic Thepade’s Ternary Block
Truncation Coding had differed from Static Thepade’s Ternary Block Truncation Coding in case of
calculating the degree value n can be 2, 4, 6… associated with threshold calculation [17].
iii.
Feature Extraction based on Edges
Edge between two regions can separate each other and help in uniquely identifying two different
regions therefore an edge is also an important feature of an image. There are many edge detection
techniques given in literature survey. For extracting edges as features firstly we apply some edge
detection techniques to highlight the edges and distinguish two separate regions. [18]Therefore firstly,
wavelet transform is used to remove noises from the image collected. [18]Secondly, some edge detection
operators such as Differential edge detection, Log edge detection, Canny edge detection and Binary
morphology are analyzed.
B.
Classification
When there is vast database of images which contains many different types of images like
mountains, rivers, planes, animals, humans, etc. is essential to classify them in to its appropriate
categories or appropriate class of images in order to manage and organize the collection of images in a
database [19]. The image classification is of two types:
a. Supervised Classification
In supervised classification the categories or classes are known before we classify the new input data. A
supervised and systematized database with limited number of major classes [20] can be a useful tool for
increased competence in searching of images from an image database.
A step to supervised classification involves:
I.
Prepare Training Data Set
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
II.
Extract Features
III.
Classify Image
b. Unsupervised Classification
In unsupervised classification the categories and classes are not known prior to classification.
Training examples are not provided in unsupervised classification.
A step to unsupervised classification involves:
I.
Generate Clusters
II.
Classify Clusters
In literature survey, the techniques and the algorithms mentioned are for supervised classification.
Also classification is a data mining technique which has a set of predefined classes and determines to
which class a new object belongs [21]. There are many data mining classifiers like Bayes, Function, Rule,
Lazy, Meta and Decision Trees. Bayes method is also used for classifying the data in data mining [22].
Neural networks and Regression are the concepts used by Function Classifier [23]. Lazy learners store the
training instances and do no real work until time of classification [23]. Decision Tress specifies the
sequence of decision that needs to be made along with the resulting recommendation [23].
3. CONCLUSION
This paper provides knowledge to several classification techniques. Mainly the image
classification can be categorized into two types Supervised Classification and Unsupervised
Classification. Feature extraction is a major step in image classification. Features can be the individual
properties or uniqueness of an image which differs from other like color, shape, texture, pattern, size, etc.
Feature extraction techniques using energy of content, color and edge are surveyed in this paper.
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