Thesis Abstract

Abstract
The rapid growth of digital image data in various domains has led to the need for efficient and accurate methods for image classification and object recognition. This thesis presents the development of a machine learning-based system for automated image classification and object recognition. The primary objective of this research is to design and implement a system that can automatically classify images into different categories and accurately identify objects within images using machine learning algorithms. The thesis begins with an introduction that provides an overview of the problem statement, research objectives, limitations, scope, significance of the study, and the structure of the thesis. The background of the study explores the existing literature on image classification and object recognition, highlighting the challenges and opportunities in this field. Chapter two presents a comprehensive literature review that covers ten key areas related to image classification and object recognition. This review examines the state-of-the-art techniques, algorithms, and tools used in the field, providing a solid foundation for the research methodology. Chapter three focuses on the research methodology, detailing the steps involved in designing and implementing the machine learning-based system. The chapter discusses the dataset collection, preprocessing steps, feature extraction techniques, model selection, training, and evaluation processes. Chapter four presents an in-depth discussion of the findings obtained from the experiments conducted using the developed system. The chapter analyzes the performance metrics, such as accuracy, precision, recall, and F1 score, to evaluate the effectiveness of the system in classifying images and recognizing objects. Finally, chapter five concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting future directions for further research. The conclusion highlights the contributions of the study to the field of image classification and object recognition and emphasizes the potential applications of the developed system in real-world scenarios. In conclusion, this thesis contributes to the advancement of automated image classification and object recognition through the development of a machine learning-based system. The research findings demonstrate the effectiveness and potential of the system in accurately classifying images and recognizing objects, paving the way for future advancements in this field.

Thesis Overview