{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-empirical-study-on-the-effects-of","title":"An empirical study on the effects of different types of noise in image classification tasks","arxiv_id":"1609.02781","date":"2016-09-09","proceeding":null,"authors":["Gabriel B. Paranhos da Costa","Welinton A. Contato","Tiago S. Nazare","João E. S. Batista Neto","Moacir Ponti"],"abstract":"Image classification is one of the main research problems in computer vision\nand machine learning. Since in most real-world image classification\napplications there is no control over how the images are captured, it is\nnecessary to consider the possibility that these images might be affected by\nnoise (e.g. sensor noise in a low-quality surveillance camera). In this paper\nwe analyse the impact of three different types of noise on descriptors\nextracted by two widely used feature extraction methods (LBP and HOG) and how\ndenoising the images can help to mitigate this problem. We carry out\nexperiments on two different datasets and consider several types of noise,\nnoise levels, and denoising methods. Our results show that noise can hinder\nclassification performance considerably and make classes harder to separate.\nAlthough denoising methods were not able to reach the same performance of the\nnoise-free scenario, they improved classification results for noisy data.","url_abs":"http://arxiv.org/abs/1609.02781v1","url_pdf":"http://arxiv.org/pdf/1609.02781v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-empirical-study-on-the-effects-of","repo_url":"https://github.com/gbpcosta/wvc_2016_noise","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}