Near-duplicate detection aims at finding images from the same scene or place inside a wide collection. In this context, near-duplicate (ND) detection differs from the extensively studied image retrieval task because of its objective is finding different version of a same object in which the distinction between ND and non-ND pairs is done through a classification function while image retrieval is usually a search to retrieve semantically similar images based on a specific query. Near-duplicate discovery has many applications as social media evaluation, web content creation, image forensic and fraud detection. I created a machine learning method that exploits images' descriptors to learn similarities between image pairs.