Comparison of Different Machine Learning Algorithms for Image Classification, Thesis of Information Technology

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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.

Chapter 2: Literature Review 2.1 Overview This chapter will provide an overview of the literature on image classification and machine learning algorithms. It will include a brief history of image classification, the types of algorithms that have been used for image classification, and the performance metrics that have been used to evaluate the performance of the algorithms. 2.2 Image Classification This section will provide an overview of the history of image classification, the different types of image classification tasks, and the applications of image classification. 2.3 Machine Learning Algorithms for Image Classification This section will provide an overview of the traditional machine learning algorithms that have been used for image classification, such as k-NN and SVMs, and the deep learning algorithms, such as CNNs and RNNs. It will also discuss the advantages and disadvantages of each algorithm. 2.4 Performance Metrics This section will discuss the different performance metrics that have been used to evaluate the performance of machine learning algorithms for image classification, such as accuracy, precision, and recall.

Chapter 3: Methodology 3.1 Overview This chapter will describe the methodology used in this thesis. It will include the dataset that was used, the pre-processing of the data, the training and testing of the algorithms, and the performance metrics that were used to evaluate the performance of the algorithms. 3.2 Dataset This section will describe the dataset that was used for the experiments. It will include information on the size of the dataset, the types of images that are included, and the data pre-processing that was performed. 3.3 Algorithm Implementation This section will describe the implementation of the traditional machine learning algorithms and deep learning algorithms. It will include details on the specific algorithms that were used, the parameters that were tuned, and the training and testing procedures. 3.4 Performance Metrics This section will describe the performance metrics that were used to evaluate the performance of the algorithms. It will include a description of the calculation of each metric and the reason for its selection.

Chapter 4: Results and Analysis 4.1 Overview This chapter will present the results of the experiments and the analysis of the results. It will include a comparison of the performance of the traditional machine learning algorithms and deep learning algorithms and the analysis of the computational complexity of the algorithms. 4.2 Performance Comparison This section will present the results of the experiments and the comparison of the performance of the traditional machine learning algorithms and deep learning algorithms. It will include the performance metrics such as accuracy, precision, and recall. 4.3 Computational Complexity This section will analyze the computational complexity of the algorithms. It will include the time taken to train and test each algorithm and the memory requirements of each algorithm.

Chapter 5: Discussion 5.1 Overview This chapter will discuss the results of the study in relation to the research objectives. The discussion will include a summary of the performance of the traditional machine learning algorithms and deep learning algorithms and the computational complexity of the algorithms. 5.2 Implications The results of the study have several implications for the selection of machine learning algorithms for image classification tasks. The study highlights the need to consider the performance of the algorithms, the computational complexity, and the suitability of the algorithm for the specific task when selecting the appropriate algorithm. 5.3 Recommendations Based on the results of the study, this section will provide recommendations for the selection of the most appropriate machine learning algorithm for image classification tasks. It will also suggest areas for future research in this field.

Chapter 6: Conclusion 6.1 Overview This chapter will summarize the main findings of the study and its contributions to the field of image classification and machine learning. 6.2 Summary This section will provide a summary of the main results of the study, including the performance comparison of the traditional machine learning algorithms and deep learning algorithms, the computational complexity of the algorithms, and the implications of the results for the selection of machine learning algorithms for image classification tasks. 6.3 Contributions This thesis makes several contributions to the field of image classification and machine learning. The study provides a thorough comparison of the performance of different machine learning algorithms for image classification tasks, which can assist practitioners in selecting the most appropriate algorithm for a specific task. It also contributes to the understanding of the computational complexity of the algorithms, which can be useful for practitioners in optimizing the training and testing of the algorithms. 6.4 Future Work This section will suggest areas for future research in the field of image classification and machine learning, such as the evaluation of the algorithms on different datasets or the incorporation of other performance metrics. References: A list of all the references used in the thesis, properly cited in the appropriate format, such as APA, MLA, or IEEE. Appendices: Any additional materials that are not essential to the main text but provide supplementary information, such as code snippets, datasets, or detailed results.