Terrain Features Extraction Using Natural Computing
Techniques
Er. Harneet Kaur1,
M.Tech Student
Department of Computer
Science & Engineering, RIEIT,
Railmajra, Ropar, India
er.harneetkaur@gmail.com
Er. Gopika Kakkar2
M.Tech Student
Department of Computer
Science & Engineering, RIEIT,
Railmajra, Ropar, India
gopikakakkar@gmail.com
ABSTRACT
In this paper, we study the feature extraction methods for
Image Classification discovered till date.Image
Classification methods have been significantly developed
in the last ten years. A novel algorithm based on analysis
ofBacterial Foraging Optimization (BFO), Biogeography
Based Optimization (BBO), Fuzzy Logic, Genetic
Algorithm (GA), and Artificial Bee Colony (ABC) are
proposed to classify an image. This study applies an
experience-based approaches related to natural computing
in the field of image classification. The main advantage of
this set of algorithm over other experience-based
techniques is that its search space is vast in nature.
Keywords: Image Classification,
Computing,
Optimization,
Intelligence.
Natural
Natural
1.0 INTRODUCTION
Usually (in the past) classification of images--at least in
the area of computer vision--has been considered using
mostly static representations. Natural visual input,
however, consists of spatial-time-related patterns and
psychophysical studies back up (with proof) that the
human visual system can fully use (for profit) built-in
time-related (features/ qualities/ traits) [11].
The term image classification refers to the labelling of
images into one of some predefined categories. The
purpose of the classification process is to separate and
label all pixels in a digital image into one of (more than
two, but not a lot of) land cover classes, or "themes". This
separated and labelled data may then be used to produce
maps of the land cover present in an image. Normally,
multispectral data are used to perform the classification
and, in fact, the pattern present within the data for each
pixel is used as the number-based basis for separation and
labelling (Lillesand and Kiefer, 1994).
The goal of image classification is to identify and
show/represent, as a (like nothing else in the world) grey
level (or color), the features happening in an image in
terms of the object or type of land cover these features
actually represent on the ground.
Er. Harish Kundra3
Head of Department
Science & Engineering, RIEIT,
Railmajra, Ropar, India
hodcseit@rayatbahra.com
If one considers classification of image sequences a
possible data representation consists of individual frames,
(in other words) the simplest view-based representation
containing only raw pixel data.
This study present a second representation which takes
energetic/changing information into account. This is done
by extraction of interest points in the image (in our case,
corners) and construction of visual features by using the
corner positions together with their local pixel
neighborhoods.
A set of important features is then found by watching and
following these visual features over the input sequence.
This representation can be seen as incorporating the (in
the mind as an opinion or judgment before doing
something, seeing something, meeting someone, etc.)
knowledge of an (one after the other) image presentation
(time-related (uninterrupted, constant quality)).
Classification between the object is easy task for
machines. The raise of high capacity computers, the
availability of high quality and low price- video cameras,
and increase the need of automatic video analysis has
generated an interest in object classification algorithm. A
simple classification system consists of camera fixed high
above the interested zone, where image are captured &
consequently processed. Classification includes image
sensors, image pre-processing, object detection,
segmentation & classification or feature extraction.
Classification system consists of database that contains
predefined patterns that compares with detected object to
classify in proper category. Image classification is an
important and challenging task in various application
domains; include bio-medical imaging, biometry, videosurveillance, vehicle navigation, industrial visual
inspection, robot navigation and remote sensing [1].
The two main methods for image classification are
supervised and unsupervised classification. Supervised
classification requires prior information before testing
process and it must collected by analyst. In this analyst
identifies representative training sites for each
informational class and also here algorithm generates
decision boundaries. Commonly used supervised
classification approaches are parallelepiped, minimum
distance to mean and maximum likelihood. In
unsupervised classification, prior information is not
needed. It does not require human annotation, it is fully
automated. This algorithm identifies clusters in data and
also analyst labels clusters [2].
paradigms’ which denotes that natural intelligence can be
computed under umbrella of computational paradigms[3]
Natural computing can be defined as a means of using
nature to resolve abstruse problems whose features are as
follows:



Flexible: related to diverse problems.
Adaptive: Problem that can be related with
dynamic atmospheres through self-adaptation.
Decentralized: deprived of a central consultant.
Autonomous: can perform devoid of human
intrusion.
Optimization is a practical science which discovers the
finest values of the factors of a problem that may take
under quantified circumstances. It is usually meets
mathematical problem in all engineering restraints. It
precisely means discovering the optimum solution.
The stochastic algorithms are of two types: heuristic and
meta-heuristic. Heuristic denotes ‘to find’ or ‘to discover
by test and error’. For instance, eminence results can be
achieved in rational amount of time but it cannot be
certain that optimum elucidations are achieved.
Additionally, fortification over heuristic algorithms is
known as meta-heuristic algorithms. Meta means
‘beyond’ or ‘advanced level.
2.0 LITERATURE SURVEY
2.0.1 Image Classification Based on
Fuzzy logic
Nedeljkovic et al 2013: [4]
Fig 1.0: Flow chart depicting Natural Computing
Methodology
Natural Computing is the field of research that deals with
computational techniques that deal with natural
inspiration. It attempts to understand the world around us
in terms of information processing. It’s a highly
interdisciplinary field that connects the natural science
with computing science; both at the level of information
technology and at the level of fundamental research. Its
strength lies in its flexibility to create models that suit the
needs arising in applications (context of discovery, model
generation). In addition, it emphasize the need of intuitive
and interpretable models, which are tolerant to the
imprecision and uncertainty. Natural intelligence
computational paradigms (NICP) is a incorporation of two
stipulations ‘natural intelligence’ and ‘computational
Fuzzy logic
relatively a young theory. The major advantage of this
theory is that it allows the natural description in terms of
linguistic, problems that should be solved rather than in
terms of relationship between precise numerical values.
This advantage deals with complicated system in simple
way, this is the reason why fuzzy logic theory is widely
applied in technique. It is also possible to classify the
remotely sensed image, in such a way the certain land
cover classes are clearly represented in the resulting
image. So we can use fuzzy logic techniques to diminish
the influence of person dealing with supervised
classification. In this paper, a prior knowledge about
spectral information for certain land cover classed is used
in order to classify spot image in fuzzy logic manner. The
later was done with mat-lab fuzzy logic toolbox. Some
information needed for membership function definition
was taken from supervised maximum likelihood
classification. The idea for result comparison came from
the image works used for supervised procedure. The result
of two procedures based on both pixel by pixel techniques
were com-paired, encouraging and conclusion remark
came out.
2.0.2 Image Classification
Neuro-fuzzy Approach
using
S. Panigrahi et al 2013: [5]
Image
classification is based on two different approachs namely,
Artifical neural network and neurofuzzy system. It is seen
that neurofuzzy system is better classification technique
than ANN.The design used the discreate cosin than fron
(DCT) for feature Extraction and Artifical neural
networks and neurofuzzy system for classification.As
DCT works on gray level image,the colour image is
transformed into gray levels. A neuro-fuzzy approach was
used to take advantage of neural networks ability to learn,
membership degrees and functions fuzzy logic. This study
proves that neuro-fuzzy model performed better than
neural in classification of image of two types.
2.0.3 Biogeography based Satellite
Image Classification
H. Kundra et al 2009: [6]
Biogeography
scrutinizes the geographical dispersal of biological
creatures. The approach of the researchers is toacquire
knowledge
from
nature.
Biogeography
Based
Optimization is a flourishing nature inspired technique to
obtain the optimum solution of the problem. Satellite
image classification is an imperative task asit is the
solitarytechnique to recognizearound the land cover map
of remote areas. Though satellite images have been
classified over years using different techniques, the
scholars are constantlydiscoveringadditional strategies for
satellite image classification inorder to choose the most
suitable technique for the feature extraction task in hand.
This studyfocuses on classification of the satellite image
of a particular land cover using the concept of
Biogeography based Optimization.
2.0.4 Genetic clustering for automatic
evolution of clusters and application
to image classification
S. Bandyopadhyayet al 2002: [7]
presents the searching competence of genetic algorithms
has been demoralizedfor inevitablydevelopingthe number
of clusters as well as suitableclustering of any data set. A
new string illustration, including both real numbers and
the do not care symbol, is used to encrypt a variable
number of clusters. The Davies–Bouldin index is used
togaugethe validity of the clusters. Efficiency of the
genetic clustering scheme is explained for both artificial
and real-life data sets. The projectedtechnique is able to
discriminate some characteristic landcover types in the
image.
2.0.5 PBBO: A New Hybrid algorithm
for Satellite Image Classification
H. Kundra et al 2012: [8] Many optimization
techniques have been evolved PSO and BBO are two
techniques that have been widely used in swarm
optimization.PSO is better than many genetic
algorithms.PSO has applications in Various area like
optimization,Netural networks training,fuzzy controls etc.
BBO is based on science of biogeography.
2.0.6 A hybrid FPAB/BBO Algorithm
for Satellite Image Classification
H. Kundra et al 2010: [9]
recently, remote
sensing has been utilized for the classification of satellite
image on a very large scale. This application employs
image classification using swarm computing technique.
This study presents a novel swarm data clustering
approachgrounded upon flower pollination by artificial
bees to gather the satellite image pixels. The goal of
collecting is to split a set of data points into self-alike
groups. Those groups will be additionally classified using
Biogeography Based Optimization. The outcomes indicate
that highly precise classification of the satellite image is
attained by using the projeced algorithm.
2.0.7
Hyperspectral
image
classification incorporating bacterial
foraging-optimized spectral weighting
A. Chakrabarty et al 2012: [10] the study
refer to the progression of a hyperspectral image
classification structure using support vector machines
(SVM) with abnormally weighted kernels. The kernels are
fabricated during the directing phase of the SVM using
optimum spectral weights predictable using the Bacterial
Foraging Optimization (BFO) algorithm, a popular stateof-the-art stochastic optimization algorithm. The
augmented kernel tasks are then in the SVM pattern for
bi-classification of pixels in hyperspectral images.
Conclusively, the use of the BFO-based technique is
endorseddue to its superior functioning, in contrast to
other modern stochastic bio-inspired algorithms.
2.0.8 Satellite Image Classification by
Hybridization of FPAB Algorithm
and Bacterial Chemotaxis
P. Singh et al 2011: [11]
Bacterial Foraging
Optimization (BFO) has been extensively accepted as a
universal optimization technique. This method is
projected by K.M. Passino in 2002 to solve complex
problems of the real world. This study aims to categorize
the satellite image using the theory of Bacterial Foraging
Optimization. One importantphase in BFO is the
calculationof Chemotaxis, where a bacterium takes stages
over the foraging landscape in order to attain regions with
high nutrient content. This new-fangledtechniqueexhibits
an enhanced highly accurate results for the classification
of satellite image when the projected algorithm is used.
2.0.9 Extended Biogeography Based
Optimization for Natural Terrain
Feature Classification from Satellite
Remote Sensing Images
V. K. Panchal et al 2011: [12]
Remote
sensing image classification in latest years has been a
thriving area of universalstudy for procuring geo-spatial
information from satellite data. In Biogeography Based
Optimization (BBO), factsdistribution between candidate
problem solutions or habitats depends on the migration
process of the ecosystem. The approvedmethodology
calculates the migration rate using Rank- based fitness
standards. The calculations indicate that highly accurate
land-cover features are obtained using the protracted BBO
technique.
2.0.10 Fusion of Biogeography based
optimization andArtificial bee colony
for identification of NaturalTerrain
Features
H. Kundra et al 2012: [13]
Swarm
Intelligence methods accelerate the pattern and
collimation of the notablecapability of group associates to
aim and learn in abackground of eventuality and
corrigendum from their nobles by sharing evidence. This
researchpresents aninnovativemethod of combination of
two intelligent techniques generally to enhance the
performance of a single intelligent technique by means of
information
contribution.
Biogeography-based
optimization (BBO) is currently acquiredempirical
algorithm, which develops to be a strong contestant in
swarm intelligence with the inspiring and steady
performance. However, as BBO deprivationsinherent
property of clustering, its behavior can be substituted with
the honey bees of artificial bee colony (ABC), a new
swarm intelligent technique. These two approaches can be
clubbed to create a new approach which is simple to
implement and gives more optimum results than the
results when BBO is used.
3.0 CONCLUSION
The above study depicts that the natural computing
techniques namely, BBO, Fuzzy, ABC, Hybrid ABC/BBO
and GA plays a vital role in image classification. The
study shows Fuzzy and ABC technique results in more
efficient
image
classification
whereas
Genetic
Algorithm(GA) is second reliable. Furthermore, reliability
decreases from hybrid ABC/BBO to the least reliable
being BBO technique.
1
4.0 FUTURE SCOPE
Natural Computing is anxious with this type of computing
in addition with its main advantage for informatics, viz.,
human-designed computing stimulated by nature. Study in
natural computing is sincerely interdisciplinary, and
therefore natural computing forms a link between
informatics and natural sciences. It has already subsidized
immensely to human-designed computing: consider, e.g.,
all the developments made through neural networks,
evolutionary algorithms, quantum computing and
molecular computing. Essentially, this investigation has
headed already to a profounder and wider empathetic of
the nature of computation.
In the impending years the expansion and the presentation
of new influential Natural Computing data dispensation
tools will become all the more vital, given the fast
emergent volume of offered biological data.
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