Analysis of Classification Approaches

ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Vol. 2, Issue 1, pp: (328-332), Month: April 2015 – September 2015, Available at: www.paperpublications.org
Page | 328
Paper Publications
Analysis of Classification Approaches
Robin Kumar
Assistant Professor, Department of Mathematics, A.S. College, Khanna, Punjab, India
Abstract: Object Classification is an important task within the field of computer vision. Image classification refers
to the labelling of images into one of a number of predefined categories. Classification includes image sensors,
image pre-processing, object detection, object segmentation, feature extraction and object classification. Many
classification techniques have been developed for image classification. In this survey various classification
techniques are considered; Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM)
and Fuzzy Classification.
Keywords: Image Classification, Artificial Neural Network, Decision Tree, Support Vector Machine, Fuzzy
Classifier.
I. INTRODUCTION
Classification between the objects is easy task for humans but it has proved to be a complex problem for machines. The
raise of high-capacity computers, the availability of high quality and low-priced video cameras, and the increasing need
for automatic video analysis has generated an interest in object classification algorithms. A simple classification system
consists of a camera fixed high above the interested zone, where images are captured and consequently processed.
Classification includes image sensors, image preprocessing, object detection, object segmentation, feature extraction and
object classification. Classification system consists of database that contains predefined patterns that compares with
detected object to classify in to proper category. Image classification is an important and challenging task in various
application domains, including biomedical imaging, biometry, video surveillance, vehicle navigation, industrial visual
inspection, robot navigation, and remote sensing.
Classification process consists of following steps:
A. Pre-processing- atmospheric correction, noise removal, image transformation, main component analysis etc.
B. Detection and extraction of a object- Detection includes detection of position and other characteristics of moving
object image obtained from camera. And in extraction, from the detected object estimating the trajectory of the object in
the image plane.
C. Training: Selection of the particular attribute which best describes the pattern.
D. Classification of the object-Object classification step categorizes detected objects into predefined classes by using
suitable method that compares the image patterns with the target patterns.
II. IMAGE CLASSIFICATION APPROACHES
Various image classification approaches are defined briefly:
1) On The Basis Of Characteristic Used:
A. Shape-based:
This method makes use of the objects’ 2D spatial information. Common features used in shape-based classification
schemes are the points (centroid, set of points),primitive geometric shapes(rectangle or ellipse), skeleton, silhouette and
contour.

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Paper Publications
B. Motion-based:
This methods use temporal tracked features of objects for the classification.
2) On The Basis Of Training Sample Used:
A. Supervised Classification:
The process of using samples of known informational classes (training sets) to classify pixels of unknown identity.
Example: minimum distance to means algorithm, parallelepiped algorithm, maximum likelihood algorithm.
B. Unsupervised Classification:
In this type of classification is a method which examines a large number of unknown pixels and divides it into number of
classes based on natural groupings present in the image values. Computer determines spectrally separable class and then
defines their information value. No extensive prior knowledge is required. Example: Kmeans clustering algorithm.
3) On The Basis Of Assumption of Parameter on Data:
A. Parametric classifier:
The parameters like mean vector and covariance matrix are used. There is an assumption of Gaussian distribution. The
parameters like mean vector and covariance matrix are frequently generated from training samples. Example: Maximum
likelihood, linear discriminant analysis.
B. Non Parametric classifier:
There is no assumption about the data. Non-parametric classifiers do not make use of statistical parameters to calculate
class separation. Example: Artificial neural network, support vector machine, decision tree classifier, expert system.
4) On The Basis Of Pixel Information Used:
A. Per pixel classifier:
Conventional classifier generates a signature by using the combination of the spectra of all training-set pixels from a
given feature. The contributions of all materials present in the training-set pixels is present in the resulting signature. It
can be parametric or nonparametric the accuracy may not meet up because of the impact of the mixed pixel problem.
Example: maximum likelihood, ANN, support vector machine and minimum distance.
B. Subpixel classifiers:
The spectral value of each pixel is assumed to be a linear or non-linear combination of defined pure materials called end
members, providing proportional membership of each pixel to each end member. Subpixel classifier has the capability to
handle the mixed pixel problem, suitable for medium and coarse spatial resolution images. Example: spectral mixture
analysis, subpixel classifier, Fuzzy-set classifiers.
C. Per-field classifier:
The per-field classifier is intended to handle the problem of environmental heterogeneity, and also improves the
classification accuracy. Generally used by GIS-based classification approaches.
D. Object-oriented classifiers:
Pixels of the image are united into objects and then classification is performed on the basis of objects. It involves 2 stages:
image segmentation and image classification Image segmentation unites pixels into objects, and a classification is then
implemented on the basis of objects. Example: e Cognition.
5) On The Basis Of Number of Outputs For Each Spatial Element:
A. Hard Classification:
Also known as crisp classification, in this each pixel is required or forced to show membership to a single class.eg
maximum likelihood, minimum distance, artificial neural network, decision tree, and support vector machine.

ISSN 2350-1022
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Paper Publications
B. Soft classification:
It is also known as fuzzy classification In this each pixel may exhibit numerous and partial class membership. Produces
more accurate result.
6) On The Basis Of Spatial Information:
A. Spectral Classifiers:
This image classification uses pure spectral information .Example: Maximum likelihood, minimum distance, artificial
neural network.
B. Contextual Classifiers:
This image classification uses the spatially neighbouring pixel information. Example: frequency-based contextual
classifier.
C. Spectral-contextual classifiers:
This classification uses both spectral and spatial information initial classification images are generated using parametric or
non-parametric classifiers and then contextual classifiers are implemented in the classified images. Example: combination
of parametric or non-parametric and contextual algorithms.
7) Multiple classifiers approach:
Different classifiers have their own advantages and disadvantages. In this approach different classifiers are combined
.some of the method for combining multiple classifier are: Voting rules, Bayesian formalism, evidential reasoning,
multiple neural network.
III. IMAGE CLASSIFICATION TECHNIQUES
TABLE I DIFFERENT TECHNIQUES FOR CLASSIFICATION
Classification
method
Description Characteristics
Artificial
Neural
network
ANN is a type of artificial intelligence that imitates some functions of
the person mind.
ANN has a normal tendency for storing experiential knowledge.
An ANN consists of a sequence of layers; each layer consists of a set
of neurones.
All neurones of every layer are linked by weighted connections to all
neurones on the preceding and succeeding layers.
It uses Nonparametric
approach.
Performance and accuracy
depends upon the network
structure and number of inputs
Decision
tree
DT calculates class membership by repeatedly partitioning a dataset
into uniform subsets Hierarchical classifier permits the acceptations
and rejection of class labels at each intermediary stage.
This method consists of 3 parts: Partitioning the nodes, find the
terminal nodes and allocation of class label to terminal nodes
DT are based on hierarchical
rule based method and use
Nonparametric approach.
Support
Vector
Machine
A support vector machine builds a hyper plane or set of hyper planes
in a high- or infinite dimensional space, used for classification.
Good separation is achieved by the hyper plane that has the largest
distance to the nearest training data point of any class (functional
margin), generally larger the margin lower the generalization error of
the classifier.
SVM uses Nonparametric
With binary classifier approach
and can handle more input data
very efficiently.
depends upon the hyper plane
selection and kernel parameter.
Fuzzy
Measure
In Fuzzy classification, various stochastic associations are
determined to describe characteristics of an image. The various types
of stochastic are combined (set of properties) in which the members
of this set of properties are fuzzy in nature. It provides the
opportunity to describe different categories of stochastic
characteristics in the similar form.
It uses Stochastic approach.
depends upon the threshold
selection and fuzzy integral.

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Paper Publications
TABLE II Advantages and Disadvantages of Different Classification Techniques
Classification
method
Advantages Disadvantages
Artificial Neural
network
 It is a non-parametric classifier.
 It is an universal functional approximator with
arbitrary accuracy.
 capable to present functions such as OR, AND,
NOT
 It is a data driven self-adaptive technique
 efficiently handles noisy inputs
 Computation rate is high
 It is semantically poor.
 The training of ANN is time
taking.
 Problem of over fitting.
 Difficult in choosing the type
network architecture.
Decision tree  Can handle nonparametric training data
 Does not required an extensive design and
training.
 Provides hierarchical associations between input
variables to forecast class membership and provides
a set of rules n are easy to interpret.
 Simple and computational efficiency is good.
 The usage of hyperplane decision
boundaries parallel to the feature axes
may restrict their use in which classes
are clearly distinguishable.
 Becomes complex calculation when
various values are undecided and/or
when various outcomes are correlated.
Support Vector
Machine
 It gains flexibility in the choice of the form of the
threshold.
 Contains a nonlinear transformation.
 It provides a good generalization capability.
 The problem of over fitting is eliminated.
 Reduction in computational complexity.
 Simple to manage decision rule complexity and
Error frequency.
 Result transparency is low.
 Training is time consuming.
 Structure of algorithm is difficult to
understand
 Determination of optimal
parameters is not easy when there is
nonlinearly separable training data.
Fuzzy Measure  Efficiently handles uncertainty.
 Properties are describe by identifying various
stochastic relationships.
 Without priori knowledge output is
not good
 Precise solutions depend upon
direction of decision.
IV. CONCLUSION
This paper attempts to study and provides a brief knowledge about the different image classification approaches and
different classification methods. Most common approaches for image classification can be categories as supervised and
unsupervised, or parametric and nonparametric or object-oriented, subpixel, per-pixel and perfield or spectral classifiers,
contextual classifiers and spectral-contextual classifiers or hard and soft classification. This survey gives theoretical
knowledge about different classification methods and provides the advantages and disadvantages of various classification
methods.
REFERENCES
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No. 5, May 2012
[3] Sonal Athavale,Neelabh Sao,"Classification On Movingobjec Trajectories",International Journal Of Advanced
Technology & Engineering Research (Ijater) Issn No: 2250-3536 Volume 2, Issue 2, May 2012

ISSN 2350-1022
Page | 332
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Analysis of Classification Approaches

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