A Comprehensive Evaluation of Machine Learning and Deep Learning Methods for Image Recognition

Abstract

Image processing is a critical aspect of image recognition, and traditional machine learning models such as K-Nearest Neighbors (KNN), Bayesian networks, and Support Vector Machines (SVM) have demonstrated various advantages in this field. However, the introduction of deep learning algorithms, particularly Convolutional Neural Networks (CNNs), has significantly enhanced the efficiency and accuracy of image recognition by reducing human intervention in feature extraction. CNNs offer a simplified structure, fast learning capabilities, and high recognition rates, making them widely applicable in image processing and pattern recognition. This paper highlights the superior performance of CNNs compared to traditional machine learning methods and discusses their potential for further research in improving recognition accuracy in future applications.