Bachelor of Science and Information Technology
Comparison of Different Machine Learning Algorithms for Image Classification
BSIT Thesis Documentation
Abstract:
Image classification is a crucial task in the field of computer vision and has a wide range of applications
such as object recognition, facial recognition, and medical image analysis. Machine learning algorithms
have been widely used for image classification tasks, but the selection of the appropriate algorithm for a
particular task is not straightforward. The goal of this thesis is to compare the performance of different
machine learning algorithms for image classification tasks. The algorithms compared will include
traditional machine learning algorithms such as k-Nearest Neighbors (k-NN) and Support Vector
Machines (SVMs) and deep learning algorithms such as Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs). The comparison will be based on several performance metrics such
as accuracy, precision, and recall. The thesis will also analyze the computational complexity of the
algorithms.
Chapter 1: Introduction
1.1 Background
Image classification is a crucial task in the field of computer vision and has a wide range of applications
such as object recognition, facial recognition, and medical image analysis. Machine learning algorithms
have been widely used for image classification tasks, but the selection of the appropriate algorithm for a
particular task is not straightforward.
1.2 Problem Statement
There are several machine learning algorithms that can be used for image classification tasks, but it is
not clear which algorithm is the most appropriate for a particular task. The performance of different
algorithms can vary depending on the specific task and dataset, and it is important to compare the
performance of different algorithms to select the best one.
1.3 Objectives
The main objective of this thesis is to compare the performance of different machine learning algorithms
for image classification tasks. The specific objectives are:
, To compare the performance of traditional machine learning algorithms such as k-NN and SVMs with
deep learning algorithms such as CNNs and RNNs
To analyze the computational complexity of the algorithms
To evaluate the performance of the algorithms using performance metrics such as accuracy, precision,
and recall.
To provide recommendations for the selection of the most appropriate algorithm for a particular image
classification task.
1.4 Scope
The scope of this thesis will include the comparison of the performance of traditional machine learning
algorithms and deep learning algorithms for image classification tasks using a publicly available dataset.
The comparison will be based on several performance metrics and the computational complexity of the
algorithms.
Comparison of Different Machine Learning Algorithms for Image Classification
BSIT Thesis Documentation
Abstract:
Image classification is a crucial task in the field of computer vision and has a wide range of applications
such as object recognition, facial recognition, and medical image analysis. Machine learning algorithms
have been widely used for image classification tasks, but the selection of the appropriate algorithm for a
particular task is not straightforward. The goal of this thesis is to compare the performance of different
machine learning algorithms for image classification tasks. The algorithms compared will include
traditional machine learning algorithms such as k-Nearest Neighbors (k-NN) and Support Vector
Machines (SVMs) and deep learning algorithms such as Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs). The comparison will be based on several performance metrics such
as accuracy, precision, and recall. The thesis will also analyze the computational complexity of the
algorithms.
Chapter 1: Introduction
1.1 Background
Image classification is a crucial task in the field of computer vision and has a wide range of applications
such as object recognition, facial recognition, and medical image analysis. Machine learning algorithms
have been widely used for image classification tasks, but the selection of the appropriate algorithm for a
particular task is not straightforward.
1.2 Problem Statement
There are several machine learning algorithms that can be used for image classification tasks, but it is
not clear which algorithm is the most appropriate for a particular task. The performance of different
algorithms can vary depending on the specific task and dataset, and it is important to compare the
performance of different algorithms to select the best one.
1.3 Objectives
The main objective of this thesis is to compare the performance of different machine learning algorithms
for image classification tasks. The specific objectives are:
, To compare the performance of traditional machine learning algorithms such as k-NN and SVMs with
deep learning algorithms such as CNNs and RNNs
To analyze the computational complexity of the algorithms
To evaluate the performance of the algorithms using performance metrics such as accuracy, precision,
and recall.
To provide recommendations for the selection of the most appropriate algorithm for a particular image
classification task.
1.4 Scope
The scope of this thesis will include the comparison of the performance of traditional machine learning
algorithms and deep learning algorithms for image classification tasks using a publicly available dataset.
The comparison will be based on several performance metrics and the computational complexity of the
algorithms.
