Overview of Algorithms for Image Recognition

Abstract

The significance of image recognition technology is highlighted by its wide applications in fields such as security, medical image analysis, and data analysis. Its growing popularity reflects advancements in research. Traditional machine learning methods have markedly improved feature extraction, while deep learning techniques have advanced significantly due to the application of various neural networks. This paper reviews algorithms and systems for image recognition, covering both traditional and deep learning methods. It provides extensive descriptions of classification and object detection techniques involving feature extraction, convolutional neural network designs, and neuron activation functions. The focus extends to traditional algorithms like k-nearest neighbor, support vector machine, Naive Bayes, and parallel cascade selection. Additionally, it explores various deep learning approaches for image interpretation, detailing different convolutional network dimensions and neuron model constructions. The paper concludes by illustrating algorithms with application examples and clarifying the differences between traditional methods and deep learning.