=Paper=
{{Paper
|id=Vol-3080/paper21
|storemode=property
|title=Exploring Convolutional Neural Network in Computer Vision-based Image Classification
|pdfUrl=https://ceur-ws.org/Vol-3080/21.pdf
|volume=Vol-3080
|authors=Parnit Kaur,Sunil K Singh,Inderpreet Singh,Sudhakar Kumar
}}
==Exploring Convolutional Neural Network in Computer Vision-based Image Classification==
https://ceur-ws.org/Vol-3080/21.pdf
Exploring Convolutional Neural Network in Computer Vision-
based Image Classification
Parnit Kaur1, Sunil K. Singh2, Inderpreet Singh3* and Sudhakar Kumar4
1,2,3,4
Chandigarh College of Engineering and Technology, Chandigarh, India
Abstract
AI consists of a large number of domains, but Machine Learning is the most prominent one.
Deep learning, a subset of machine learning, is now overtaking all the various prediction
techniques and algorithms. Computer vision is one of those domains which have benefited
most from deep learning techniques. One of the most industrially used applications of computer
vision is image classification and recognition. Deep learning methods have been proven to give
state-of-the-art results in image classification utilities. Convolutional Neural Networks (CNN)
is the most implemented deep learning algorithms in image classification problems. This paper
gives a detailed survey of the implementation of deep learning-based CNN in image
classification problems under the domain of computer vision. The paper introduces all the
concepts of AI, machine learning, deep learning, CNN, and image classification.
Keywords 1
Deep Learning, Convolutional Neural Networks, Image Classification, Computer Vision
1. Introduction
Artificial intelligence (AI) is an expansive range of software engineering identified with building
machines capable of accomplishing tasks that require extensive human insight [1]. AI identifies with
different parts of science with a lot of methodologies [2-5] , however facilitation in AI and profound
learning are making a model change in pretty much every area of the innovative business. Simulated
intelligence expands upon the idea that the human brain can be characterized such that a machine can
undoubtedly mimic it and perform undertakings, from the most straightforward to those that are
considerably more compound [6].
Image Recognition, which is one amongst the undertakings in which deep neural networks (DNNs)
dominate [6]. Neural organizations are figuring frameworks intended to perceive relational data in
images of the designs. The main engineering utilized for image classification is Convolutional Neural
Networks (CNNs) [7]. CNNs comprise a few layers with little neuron assortments, every one of them
seeing little pieces of a picture. The outcomes from every one of the assortments in a layer in part cover
in an approach to make the whole picture portrayal. The layer underneath then rehashes this cycle on
the new picture portrayal, permitting the framework to find out about the picture synthesis. In this paper,
a detailed discussion on the application of deep learning based convolutional neural networks in the
domain of image classification is presented. The paper describes various image classification
applications and the state-of-art CNNs utilized in these applications. A detailed study on the
introductory terms AI components i.e., machine learning and deep learning along with computer vision
is also presented to understand the background of CNNs in image classification.
1
International Conference on Smart Systems and Advanced Computing (Syscom-2021), December 25–26, 2021
EMAIL: parnitkaur.pk@gmail.com (A. 1); sksingh@ccet.ac.in (A. 2); inderpreet221099@gmail.com (A. 3); sudhakar@ccet.ac.in (A. 4);
ORCID: 0000-0001-6034-5316 (A. 1); 0000-0003-4876-7190 (A. 2); 0000-0002-3834-025X (A. 3); 0000-0001-7928-4234 (A. 4)
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
The rest of the paper is structured as follows. Section. 2 outlines the Background of AI components
(machine learning and deep learning) and the domain of computer vision. Detailed introduction of
image classification is demonstrated in Section. 3. Section. 4 describes the Convolutional Neural
Networks and its famous architecture. Section. 5. demonstrates the usage of CNN infamous image
classification problems which includes traffic sign classification and plant species recognition. Finally,
the challenges and future scope of image classification and CNNs are described in Section 6.
2. Background
Explicit uses of AI incorporate normal language handling, master frameworks, discourse
acknowledgment, and computer vision, ML is one of the most prominent subsets of AI. ML alludes to
an AI framework that can self-learn depending on the calculation. Explicit uses of AI incorporate
normal language handling, master frameworks, discourse acknowledgment, and computer vision, ML
is one of the most prominent subsets of AI. ML alludes to an AI framework that can self-learn depending
on the calculation. DL is an ML subset that is usually applied to massive informational collections.
2.1. Deep Learning
Deep learning (DL) also comprehended as deep neural learning or deep neural network is a present-
day and sparking field of ML. It is a subfield of machine learning, and its algorithms are enlightened
by artificial neural networks. DL is an AI function that tries to imitate the workings of the mortal brain
in processing data and creating configurations for utility in conclusion substance. Deep learning models
have different layers which include the input layer, hidden dense layer, and finally the output layer. A
basic deep learning model is shown in Fig. 1.
Figure 1: Deep learning model
DL is the utmost efficiency, supervised, period, and price-effective machine learning strategy. DL
isn't a definite knowledge approach, but it sticks to multicolored procedures and geomorphology that
may or may not be applicable for an immense speculum of complex issues. The method understands
the explicative and differential features hierarchically [9]. DL methodologies have made an important
enhancement with discernible accomplishment in an extensive diversity of operations with beneficial
security tools. It's contemplated to be the stylish choice for finding and analyzing complex frames in
high-dimensional data by employing a backpropagation algorithm [10].
2.2. Computer vision
An interdisciplinary scientific extent that handles in what way computers can escalate great- echelon
comprehension from digital images or tapes is computer vision. It understands and automates
assignments that the earthborn visual system can do [11]. Computer vision assignments carry
techniques for processing, dissecting, and concluding digital pictures, and stock of primary-dimensional
data from the unaffected globe in the league to frame numerical or representative data, for the specimen
in the arrangement of verdicts [12]. Comprehending this terrain means the transfiguration of graphic
pictures into descriptions of the world that bring in common sense to study processions and can raise
suitable ambition [13].
Computer vision permits us to check, bit by bit, that each piece is in its place, or toward the finish
of the interaction, that the last get together is right. This application is valuable for the get-together of
apparatus, hardware, electronic sheets, or pre-congregations with a ton of intricacy. There are numerous
applications of computer vision, some of the major applications under consideration are as follows:
● Object Detection: Article recognition is a computational machine vision procedure that allows
us to identify and detect objects in a picture or video. With this sort of recognizable proof and
restriction, object recognition can be taken advantage of to include objects in a scene and
determine and trace their exact areas, all while accurately marking them [14].
● Gesture Recognition: Gesture recognition is a sort of perceptual processing UI that allows
computational machines to find and decipher human motions as orders [15]. The overall
meaning of motion acknowledgment is the capacity of a computational machine to get motions
and implement orders dependent on those motions.
● Image Classification: Image classification is one of those computer vision applications which
is of very high industrial importance [16]. Image Classification is the essential space, wherein
profound neural organizations assume the main part in picture examination. The picture
arrangement acknowledges the given information pictures and delivers yield characterization
for distinguishing if the illness is available [17]. The characterization cycle plans to classify
every pixel in an advanced picture into one of a few land cover classes, or "topics". This
arranged data can be applied to make effective aides of the land cover present in an image.
Consistently, multispectral data is used to play out the request, and, no doubt, the apparition
model present inside the data for each pixel is used as the numerical justification for the plan.
3. Image Classification
Image classification is the task where a computational machine can examine a picture and
distinguish the 'class' the picture falls under. Picture grouping is the interaction of the computational
machine investigating the picture and revealing to you what is in it. Early picture characterization
depended on crude pixel information. This implied that computational machines would separate
pictures into singular pixels. The issue is that two photos of the same thing can look altogether different.
They can have various foundations, points, presents, etcetera. This made it a remarkable test for
computational machines to effectively 'analyze' and order depictions.
Image classification has a couple of employments and tremendous potential as it fills in unwavering
quality. Self-driving vehicles use picture characterization to recognize what's around them for example,
trees, individuals, traffic signals, etc. Image classification can likewise help in medical services. The
target of image classification is to recognize and depict, as a remarkably dim level (or shading), the
highlights happening in a picture as far as the article these highlights address on the ground. Image
classification is one of the major components of computerized picture examination [18]. Deep Learning
along with IoT devices is now the most used practical implementation of classification models [19].
4. Convolutional Neural Networks (CNN)
CNN consists of several engineered layers of artificial neurons. The CNN's first echelon normally
detects abecedarian features alike as reclining, erect, and canted edges. The affair of the first echelon is
fed as input of the coming echelon, which excerpts more multifaceted attributes like corners and edges
[20]. The convolutional neural network is a grade of deep learning systems that run dominant in
prismatic computer vision jobs and attract interest across prismatic lines, including radiology [21]. The
CNN is composed of several structure blocks, parallel as difficulty layers, pooling layers, and
exhaustively connected layers, and is aimed to automatically determine spatial rankings of applications
and functionalities through a backpropagation algorithm. Impropriety with the convolutional neural
network's notions and advantages and limitations is essential to pull its possible to perfect radiologist
performance [22]. Fig. 2 demonstrates the working of a general CNN model.
Figure 2: Convolutional Neural Networks model
Convolution is progressed where the network tries to entitle the input signal by applying what it has
studied in the past. The taking labor signal is either passed on to the ensuing gentry [23]. Either the
ensuing is subsampling Inputs from the complicacy gentry can be “smoothened” to reduce the delicacy
of the defilements to noise and variations [24]. This smoothing process is called subsampling and can
be achieved by taking norms or taking the most over a sample of the signal. The activation gentry
controls how the signal flows from one gentry to the ensuing, emulating how neurons are fired in our
brain [25]. Labor signals which are forcefully associated with old references would move another
neuron, enabling signals to be propagated more effectively for recognition [26-27].
5. Image classification applications
5.1. Traffic sign classification
Traffic sign acknowledgment innovation is a basic part of the driver help framework as it predicts
the ebb and flow driving climate. With the assistance of a traffic sign acknowledgment framework, the
drivers do require consistently focusing out and about signs, and huge pressing factors and weaknesses
can be decreased. Since the convolutional neural organization (CNN), has shown unprecedented order
execution which can naturally acquire highlights gained from huge informational collections, it has
been broadly utilized in the rush hour gridlock sign acknowledgment framework.
Table 1
Traffic Sign classification performance metric analysis for different methods and models
Authors Methods Performance metric
Lu, Y., Lu, J., Zhang, S., & Hall, P. (2018). CNN, attention 90%
Traffic signal detection and classification in model
street views using an attention model [28].
Chen, L., Zhao, G., Zhou, J., & Kuang, L. CNN, MLP 98.26%
(2017). Real-Time Traffic Sign Classification
Using Combined Convolutional Neural
Networks [29].
Saini, S., Nikhil, S., Konda, K. R.B, H. S., & CNN, SVM 99.03%
Ganeshan, N. (2017). An efficient vision-
based traffic light detection for vehicles
[30].
Inderpreet Singh, Sunil Kr. Singh, Sudhakar Dropout VGG 99.53%
Kumar, Kriti Aggarwal. (2021). Dropout-VGG based
based Convolutional Neural Network for Convolutional
Traffic Sign Categorization [31]. Neural Network
5.2. Plant species recognition
Machine vision dependent on traditional picture handling strategies can be a helpful device for plant
location and recognizable proof. Plant recognizable proof is required for weed identification, herbicide
application, or other proficient synthetic spot splashing tasks. The way to fruitful recognition and
recognizable proof of plants as species types is the division of plants from foundation pixel locales.
Specifically, it is helpful to section singular leaves from the highest points of overhangs too.
Table 2
Plant species recognition performance metric analysis for different methods and models
Authors Methods Performance
metric
Mehdipour Ghazi, M., Yanikoglu, B., & CNN, AlexNet, Transfer 78.44%
Aptoula, E. (2017). Plant identification Learning, VGCNet,
using deep neural networks via GoogleNet
optimization of transfer learning
parameters [32].
Grinblat, G. L., Uzal, L. C., Larese, M. G., CNN, Central Patch 96.9%
& Granitto, P. M. (2016). Deep learning Extraction, Vein
for plant identification using vein Segmentation
morphological patterns [33].
Dyrmann, Mads, H. Karstoft and H. CNN, Batch
Midtiby. (2016). Plant species Normalization, Max 98%
classification using deep convolutional Pooling
neural network [34].
6. Open issues and future scope
There are a few problems or open issues in image classifications that are in the way of achieving
complete accuracy in a model [35]. Image classification puts forward a lot of challenges for us, some
of them are included in the process of a video, optical character recognition, Adhoc image classification,
and many more. A few points of view of one individual on camera can confound picture order
arrangements that can show one individual as a few people [36]. Solutions must combine all media into
single-individual media profiles. At the point when excess profiles are decreased, advanced examiners
would then be able to depend on the created waitlist of profiles to discover suspects, leads, and
casualties quicker [37-39]. Frequently cell phones contain screen captures of discussions from message
applications that individuals use to report discussions. Picture-to-message acknowledgment is essential
to figure out pictures where the imagined message should be searchable [40]. More applications or
scopes that account to image classification are internet of things devices for smart cities which involve
various machine learning applications [41].
CNN also has some open-ended problems, one of many difficulties in the field of CNN is to manage
the change in the information present. The human visual framework can distinguish pictures under
various points, under various foundations, under a few diverse lighting conditions. At the point when
the articles are concealed partially by different items or hued, the human visual framework discovers
signs and different snippets of data to recognize what we are seeing [42]. Making a ConvNet that can
perceive objects at a similar level as people have been demonstrated is difficult. Regardless of where
the article is available in the picture an all-around prepared ConvNet can distinguish the item present
in the picture [43]. Be that as it may, assuming the article in the picture comprises pivots and scaling,
the ConvNet will struggle to distinguish the item in the picture. It can be tackled by adding various
varieties to the picture during the preparation cycle also called Data Augmentation. Fig 3. depicts the
global image classification usage in the coming years [44].
Figure3: Global Image classification usage in the coming years
Image classification development has changed web-based portrayal with its applications in facial-
affirmation, driverless vehicles, clinical ailment recognizing confirmation, and shockingly in the space
of guidance. The destiny of picture affirmation applications is wide [45]. Extended reality has given a
very surprising perspective to 'fantasizing with your eye open'. The gaming field has started using
picture affirmation development joined with extended reality for their possible advantage, as it helps
with outfitting gamers with a functional experience [46]. Clearly, engineers have an advantage as they
can utilize picture affirmation in setting up useful gaming conditions and characters.
7. Conclusion
An extensive study is performed on image classification, CNN, and its uses. Also, there are a lot of
open issues that are further discussed. Deep learning-based ConvNets are under observation of the
paper. All the factors are kept in mind and mentioned while discussing computer vision, and its uses in
the current scenario involving the upcoming areas like IoT, AI, smart cities, etc. This paper gives a
detailed study about as discussed in the paper, image classification is the process of putting images into
categories and labeling groups of vectors or even pixels within an image based on particular aspects.
CNN is utilized for image classification and recognition as a result of its high precision. The CNN
follows a various leveled model which deals with advancing or progressing an organization, similar to
a pipe, lastly gives out a completely associated layer where every one of the neurons is associated with
each other and the yield is generated. In this paper, analysis is done for various image classification
applications using deep learning methods. The applications include face recognition, traffic sign
classification, and plant species recognition.
8. Acknowledgment
We wish to acknowledge the help provided by the technical and support staff of the CCET ACM
Student Chapter and the Department of Computer Science & Engineering, CCET (Degree Wing),
Chandigarh. We would also like to show our deep appreciation to our supervisors who helped us in the
finalization of our research paper.
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