A Comparative Study of Machine Learning Algorithms for Image Recognition in Privacy Protection and Crime Detection
Keywords:
Machine learning, image identification, comparative analysis, k-nearest neighbours, random forests, and convolutional neural networksAbstract
In this work, machine learning techniques for picture recognition are compared. With diverse applications, from object detection to facial recognition, image recognition has emerged as a key area in computer vision. Computers can evaluate and comprehend visual input thanks in large part to machine learning techniques. However, because there are so many possibilities available, choosing the best algorithm for picture recognition jobs can be difficult. The common machine learning methods for picture recognition that will be studied and assessed in this study are convolutional neural networks (CNNs), support vector machines (SVMs), and random forests (RFs). Accuracy, computational effectiveness, and resistance to noise and fluctuations in image quality are some of the criteria used in the evaluation. The results of this study will help researchers and practitioners choose the best machine learning algorithm for their particular applications by revealing the advantages and disadvantages of various image recognition methods.
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