Similar Images Finder Using Machine Learning

Abstract

Finding similar images using machine learning has become an important technique for image retrieval and organization in a numerous applications. This method involves training deep learning models to recognize visual patterns in images and extract features that can be used to identify similarities and differences between images. These features can then be used to create high-dimensional vectors that represent each image, which can be compared to find similar images. Various similarity metrics and clustering algorithms can be used to group similar images together, enabling efficient searching of large image datasets. This paper provides an outline of the methodology for finding similar images using machine learning, including data collection, preprocessing, deep learning model training, feature extraction, similarity calculation, and clustering. The paper also discusses the practical applications of this technique, such as image retrieval, image organization, and image analysis in fields such as medical research, computer vision, and machine learning. The proposed system can be a valuable tool for content creators, online platforms, and law enforcement agencies to ensure the protection of intellectual property rights and prevent unauthorized use of copyrighted images.