This paper discusses how computer vision and image classification techniques can help achieve the UN's Sustainable Development Goals by promoting green economy initiatives. It reviews research on using image binarization and feature extraction for content-based image classification. The paper finds that a local thresholding technique called Niblack's method achieves the highest accuracy for classifying images in a sample dataset. Promoting technologies like this could encourage online transactions and digitization, reducing environmental impact while achieving economic and social benefits in line with sustainable development.

1. Department of Information Technology, XISS Ranchi 1
Achieving Sustainable Development Goals using Computer
Vision
R. Selot¹, A. Gupta²
¹Student, Department of Information Technology, Xavier Institute of Social Service Ranchi,
Jharkhand, India
²Student, Department of Information Technology, Xavier Institute of Social Service Ranchi,
Jharkhand, India
Abstract
Information and Communication Technology has played a pivotal role in achieving
Sustainable Development Goals (SDG) for an improved future. The foundation for
sustainable development is based on economic development, social inclusion and
environmental protection. All these three pillars of SDG have observed massive application
of information identification using image data to ensure management of sustainability using
Information and Communication Technology (ICT). We will discuss one goal of sustainable
development which is the green economy by helping in calculating the cost of achievement of
green economy as well as maintenance cost after the development is been done in the area
which was realized in the image (with comparison with master image). The easy availability
of image data has posed significant research challenges to make it available in real time
environment to the stakeholders to ensure sustainable practices. The paper has addressed the
aforesaid issues and has introduced a novel technique for content based image classification
to enhance the contribution of ICT in achieving Sustainable Development Goals.
Keywords: Sustainable Development, green economy, Contents Based Image Identification,
Feature Extraction image binarization

2. Department of Information Technology, XISS Ranchi 2
1. Introduction:
Sustainable development as given by the
UN standards is “Development that meets
the needs of the present without
compromising the ability of generations to
meet their own needs” [9]. According to
the topic given by UN sustainable
development can be implemented in this
fast growing world with the help of
technology and science. Technology
preferred environmentally as well as which
have the better deployment with the
context of the green economy gives rise to
business opportunities as well as finance
and investment opportunities. Recent
emphasize on encouraging online payment
system by Government of India has
precisely proven this fact. The different
modes of payment in line with the green
economy include online gateways even for
offline transactions. The online gateways
require scanning of matrix barcode for
authentication of offline transactions is a
clear example of pattern recognition using
machine readable label. On the other hand
advertising and media have been evolving
paperless initiatives of spreading
information to the common mass to ensure
sustainability as well as the preservation of
the environment. The aforesaid initiatives
include advertising with the help of image
data to promote online transactions to
satisfy the three pillars of Sustainable
development goals, viz., economic
development, social inclusion, and
environmental protection. But sustainable
development goals can only be achieved
with the whole-hearted cooperation of the
common mass. Stakeholders and
customers will get attracted towards the
green economy by adopting the use of
online payments for both online and
offline transactions. As discussed the green
economy involved in online or offline
transactions is having a huge influence of
image data for successful execution.
Robust feature extraction from the image

3. Department of Information Technology, XISS Ranchi 3
data is essential for proper content based
identification has played a pivotal role in
achieving sustainable development goals
with the green economy. As we are
entering the new phase of globalization it
is essential to keep pace with the ever-
changing needs and demands of the
customer as well as to ensure sustainability
as a moral responsibility.
2. Related work and
Discussion:
Sustainable development not only by
means of online and offline economic
transaction but also with space-based
applications like environment monitoring
are heavily dependent on the huge
repository of image data. The industrial
development and the medical instruments
for detecting terminal diseases like
melanoma, breast cancer etc. have
efficiently used masked images,
mammograms etc. for early disease
detection and has decreased in mortality
rate. Recent research papers have shown
huge applications of the aforesaid
methodologies for ensuring sustainable
development goals [9]. Several feature
extraction techniques including
transformation techniques, morphological
techniques, binarization techniques, fusion
based techniques etc. have been readily
discussed and implemented by the
researchers for improvement of human
lives and well-being. [1][2] Customer
interaction with the computer system has
changed drastically due to online access to
products. Conventional means of
identifying online products are text-based
queries which have irrelevant result in the
majority due to lack of intelligent
searching. Content-based image
classification has emerged as a fruitful
alternative to this process. Feature
extraction is the fundamental stepping
stone to ensure enhanced identification
accuracy. Contemporary literature has
discussed image binarization technique as
an efficient means for feature extraction
process. [12] Binarization process is a

4. Department of Information Technology, XISS Ranchi 4
process of differentiating the foreground of
the image from its background. Three
different thresholding techniques are
available for binarization viz. global
threshold, local threshold, and mean
threshold. [1, 2] Binarization is an efficient
process to differentiate the foreground and
background of an image. This is a well-
organized practice as the image feature
extraction is done. Local thresholding
technique has been proved to be effective
for binarization of images with uneven
illumination [1]. The methods employed in
the two papers[1][2] that are referenced we
get to know about the local threshold,
standard deviation, and variance which are
the measure of dispersion and which will
help us to handle the problem of improper
illumination of the image [2]. Mean
threshold means the mean of all the RGB
value of the image so that proper threshold
can be selected to differentiate the
foreground image from the background
image. [1, 2].The proposed techniques
which are used in both the papers are viz.
first Niblack’s Method for binarization of
images and secondly the authors have
proposed another local thresholding
technique for the binarization process
which takes the mean threshold and also
the standard deviation for the calculation
of feature vector [1][2]. In this paper, we
have made a study of different binarization
techniques of feature extraction for better
precision value. The study has involved
the interdisciplinary approach to
emphasize on the application area for
content based image recognition to achieve
sustainable development goals. The
intention is to achieve good results for
sustainably. This gives rise to the green
economy as we are saving the environment
by decreasing the usage of papers both by
advertising digitally and by encouraging e-
commerce and economically as by using
the digital platform we are giving rise to
cashless transactions and e-wallets.
Customer satisfaction with image
identification with reducing irrelevant
outputs can generate mass awareness about

5. Department of Information Technology, XISS Ranchi 5
the green economy. We have taken a
public dataset named Wang database
(Figure 2) and have studied the image
identification results with different
techniques. We have observed the
technique discussed in [1] have the highest
possible accuracy of content based image
classification among all the recent
literature we have reviewed the
classification results [1, 2]. Niblack
method for image classification is better.
The Niblack technique is a local
thresholding process which is useful for
the selection of images with uneven
illumination. [1] The second paper has also
used the similar approach to enhance
classification accuracy, in this paper; IBM
SPSS has been used for statistical testing
and analysis [2] to establish the statistical
significance of the achieved classification
accuracy with respect to the state of art
classification techniques. Hypothesis
testing is also used for validating the cause
and idea of the main research. The feature
vector is generated by dividing all the
pixels into lower intensity values and
higher intensity values by comparing it
with the mean threshold value. Steps that
are taken to achieve the goal are, firstly the
image that is taken into consideration is
divided into red (R), green (G) and blue
(B) color component which is followed by
binarization process first by Niblack’s
Method and then by the algorithmic
approach [1, 2]. Then the binarized image
has different shades visible clearly. The
feature vector for higher intensity values
and for lower intensity value are then
stored for image recognition. Precision and
recall values from both the approaches are
calculated and compared. (Figure 3 and
Figure 4)

6. Department of Information Technology, XISS Ranchi 6
Figure 1: Sample image: Wang dataset
3. Comparative Study
Figure 2: Graphical illustration of
Comparison of Precision from Paper [1]
Paper [2] for Wang Dataset
Figure 3: Graphical illustration of
Comparison of Recall from Paper [1] and
Paper [2] for Wang Dataset
The above tables clearly show that the two
techniques mentioned in the paper [1, 2]
and as depicted in table 1 and table 2 we
conclude that the ANN technique is the
best as it has the highest identification rate
and precision value. The higher precision
results indicate that the customers can have
higher rate of relevant results on e-
commerce portals to identify their desired
products and also faster scanning of codes
for payment gateways can be done. This in
turn will create awareness among the mass
about the use of technology to achieve
sustainable development goals. People will
77.6
78.5
80.7
79.7
83.8
86.4
72
74
76
78
80
82
84
86
88
Precison 1 Precison 2
KNN SVM ANN
76.3
76.8
80.4
79.5
83.7
86.2
70
72
74
76
78
80
82
84
86
88
Recall 1 Recall 2
KNN SVM ANN

7. Department of Information Technology, XISS Ranchi 7
feel interested and curious to use online
techniques since they can have wider
varieties with lesser buyer pressure. The
deployed techniques on the Wang dataset
have been reviewed and discussed to show
the advancement and application of
technology for environment protection and
green economy encouragement.
Fig. 4 Comparison of Precision and Recall
Table 1. Comparison of Precision [1]
Table 2. Comparison of Recall [1]
78.5
76.8
79.7 79.5
86.4 86.2
72
74
76
78
80
82
84
86
88
Precicion Recall
KNN SVM ANN
Categories KNN SVM ANN
Tribals 52 64 81
Sea Beach 68 75 80
Gothic
Structure
55 52 65
Bus 73 72 78
Dinosaur 100 100 100
Elephant 91 93 95
Roses 81 90 93
Horses 91 90 93
Mountains 71 81 88
Food 86 78 89
Average 76.8 79.5 86.2
Categories KNN SVM ANN
Tribal 85.2 78 80.2
Sea Beach 81.9 76.5 82.5
Gothic
Structure
60.4 63.4 73.9
Bus 59.3 67.3 70.9
Dinosaur 100 100 97.1
Elephant 68.9 73.2 81.2
Roses 94.2 85.7 93.9
Horses 93.8 98.9 94.9
Mountains 77.2 82.7 95.7
Food 63.7 70.9 93.7
Average 78.5 79.7 86.4

8. Department of Information Technology, XISS Ranchi 8
4. Conclusion
Sustainable development is the need of the
hour and should be practiced by the people
on the everyday basis. In this paper, we
have discussed on achieving sustainability
moreover green economy by the use image
recognition and image classification on the
basis of feature extraction. Nowadays the
online methods are used for the offline
transactions for example e-wallets used for
buying eatables and groceries. This has
given rise to sustainable development and
also fulfilled a bigger part of the green
economy by making the country
economically stable and also encouraging
paperless and cashless transactions. In this
paper, we have reviewed several papers
which were in line with our idea of
research and studied different feature
extraction method and concluded with the
best method for image classification. This
paper is made with the root idea of
achieving sustainable development with
information technology at large.
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