International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023
p-ISSN: 2395-0072
www.irjet.net
From Pixels to Understanding: Deep Learning's Impact on Image Classification and Recognition Kush Mehta1 , Venu Chaudhari2 1Student, Computer Engineering, K.J. Somaiya Institute of Technology, Sion, India
2Student, Computer Engineering, K.J. Somaiya Institute of Technology, Sion, India
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Abstract - This paper discusses the crucial role that
allows for an improvement in accuracy. The amount of computation required by the training network likewise dramatically increases as the number of layers and the number of nodes in each layer grows.
machine learning, and particularly deep learning, play in artificial intelligence. It emphasizes the importance of deep learning for classifying and identifying photos. The study compares deep learning progress to that of traditional machine learning methods. Other deep learning network topologies are addressed in depth, including deep belief networks, convolutional neural networks, and recursive neural networks. The application of deep learning for image recognition and classification is explained, along with issues and solutions. The paper's conclusion summarizes and projects the current state of deep learning for image recognition and classification. Overall, deep learning has shown exceptional performance in tasks like photo categorization and has great potential for improving image recognition in computer vision thanks to its neural network simulation of the human brain.
Strengthening the research on image recognition technology is essential for the advancement of artificial intelligence and computer vision, and deep learning is a key technical tool in the field of image recognition with a wide range of application possibilities. Deep learning, in its simplest form, is a technology that simulates and analyzes the human brain through the creation of deep neural networks, or that learns and interprets pertinent data by imitating the human brain. After examining, comprehending, and processing connected images, image recognition is a technology that can identify distinct patterns of objects and targets. Traditional picture classification techniques, such those relying on manual annotation and key point description, are not only tedious and time-consuming, but they are also significantly influenced by subjective human variables, which results in low classification accuracy. The multi feature fusion and deep learning-based image classification algorithm can produce more effective image classification results. Images now play a significant role in how people receive and send information since they are vital information carriers. It is crucial to locate the users' required photos quickly because, for big photographs, the number of images that each user actually needs is quite low. For quick and efficient extraction and analysis of image semantic information, an efficient deep learning technique has significant scientific significance. The goal of this work is to further improve the application effect of deep learning so that it can play a bigger role in the field of picture recognition. It does this by analyzing the research and applications of deep learning in image recognition.
Key Words: Deep Learning, Computer vision, Artificial Intelligence, Image Recognition and Classification
1.INTRODUCTION A significant number of photos are produced in every field every day due to the expanding variety of image capture equipment and the ongoing technological maturation of image processing, which causes the images to exist in a vast form [1]. A significant area of study in the field of computer vision is image classification. To achieve the goal of identifying various categories of images, an image classification algorithm organizes the features taken from the original image. Artificial intelligence is widely used in many facets of society, which not only raises the degree of industrial modernization and intelligence but also significantly enhances the quality of life for people [2]. Many unlabeled picture data flow onto the network as a result of the development of Internet technology, and deep learning algorithms may extract abstract feature representation from these unlabeled data by utilizing multi-layer nonlinear transformation for image classification. Deep learning is an essential part of machine learning and has a significant impact on the advancement of contemporary artificial intelligence. Deep learning is being used to classify images more accurately, and in certain cases, its performance is even better than that of humans. The massive volume of calculations is what
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2.TYPICAL STRUCTURE
DEEP
LEARNING
NETWORK
2.1 Deep Belief Network Deep belief networks, one of the most used frameworks for deep learning algorithms, were first presented by Geoffrey Hinton in 2006. Several layers of restricted boltzmann machines are built on top of one another to create a deep belief network by using the hidden layer of
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