{{Short description|Process supporting machine learning}} '''Data annotation''' is the process of labeling or tagging relevant metadata within a dataset to enable machines to interpret the data accurately. The dataset can take various forms, including images, audio files, video footage, or text.
== Applications == Data is a fundamental component in the development of artificial intelligence (AI). Training AI models, particularly in computer vision and natural language processing, requires large volumes of annotated data.<ref>{{Cite news |title=Data Annotation |url=https://labelyourdata.com/articles/data-annotation |archive-url=https://web.archive.org/web/20241207154159/https://labelyourdata.com/articles/data-annotation |archive-date=2024-12-07 |access-date=2025-03-11 |language=en-GB |url-status=live }}</ref> Proper annotation ensures that machine learning algorithms can recognize patterns and make accurate predictions.<ref>{{Cite web |last=Sajid |first=Haziqa |date=2024-12-18 |title=The Hidden Role of Data Annotation in Everyday AI Tools |url=https://www.unite.ai/the-hidden-role-of-data-annotation-in-everyday-ai-tools/ |access-date=2025-03-11 |website=Unite.AI |language=en-US}}</ref> Common types of data annotation include classification, bounding boxes, semantic segmentation, and keypoint annotation.<ref>{{Cite book |last1=Freire |first1=Juliana |url=https://books.google.com/books?id=W6-rCAAAQBAJ&q=%22Data+annotation%22 |title=Provenance and Annotation of Data and Processes: Second International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, UT, USA, June 17-18, 2008 |last2=Koop |first2=David |date=2008-11-19 |publisher=Springer |isbn=978-3-540-89965-5 |language=en}}</ref>
Data annotation is used in AI-driven fields, including healthcare, autonomous vehicles, retail, security, and entertainment. By accurately labeling data, machine learning models can perform complex tasks such as object detection, sentiment analysis, and speech recognition with greater precision.<ref>{{Cite web |date=2023-09-12 |title=The Complete Guide to Data Annotation |url=https://www.anolytics.ai/blog/data-annotation-guide |access-date=2025-03-11 |website=Anolytics |language=en}}</ref><ref>{{Cite book |last=Spair |first=Rick |url=https://books.google.com/books?id=tNAOEQAAQBAJ&dq=%22Data+annotation%22+%22AI%22&pg=PT399 |title=200 Tips for Mastering Generative AI |publisher=Rick Spair |language=en}}</ref>
This growing demand has led to the emergence of specialized sectors and platforms dedicated to [https://aitrainer.work AI training] and human-in-the-loop workflows, which often utilize Reinforcement Learning from Human Feedback (RLHF) to refine model behavior. <ref>{{cite web |title=What is AI Training? The Ultimate Beginner's Guide (2026) |url=https://aitrainer.work/guides/what-is-ai-training-beginner-guide |website=aitrainer.work |language=en |date=10 February 2026}}</ref>
== In computer vision ==
=== Image classification === Image classification, also known as image categorization, involves assigning predefined labels to images. Machine learning algorithms trained on classified images can later recognize objects and differentiate between categories. For instance, an AI model trained to recognize furniture styles can distinguish between Georgian and Rococo armchairs.<ref>{{Cite book |last=Ghani |first=Arfan |url=https://books.google.com/books?id=1v0XEQAAQBAJ&q=%22Data+annotation%22+%22Image+classification%22 |title=Innovations in Computer Vision and Data Classification: From Pandemic Data Analysis to Environmental and Health Monitoring |date=2024 |publisher=Springer Nature |isbn=978-3-031-60140-8 |language=en}}</ref>
=== Semantic segmentation === Semantic segmentation assigns each pixel in an image to a specific class, such as trees, vehicles, humans, or buildings. This type of annotation enables machine learning models to differentiate objects by grouping similar pixels, allowing for a detailed understanding of an image.<ref>{{Cite book |last=Antonacopoulos |first=Apostolos |url=https://books.google.com/books?id=DxI1EQAAQBAJ&dq=%22Data+annotation%22+%22Semantic+segmentation%22&pg=PA2 |title=Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part XVIII. |date=2 December 2024 |publisher=Springer Nature |isbn=978-3-031-78456-9 |language=en}}</ref><ref>{{Cite book |last1=Lei |first1=Tao |url=https://books.google.com/books?id=VauIEAAAQBAJ&dq=%22Data+annotation%22+%22Semantic+segmentation%22&pg=RA1-PA15 |title=Image Segmentation: Principles, Techniques, and Applications |last2=Nandi |first2=Asoke K. |date=2022-10-03 |publisher=John Wiley & Sons |isbn=978-1-119-85900-0 |language=en}}</ref>
=== Bounding boxes === Bounding box annotation involves drawing rectangular boxes around objects in an image. This technique is commonly used in autonomous driving, security surveillance, and retail analytics to detect and classify objects such as pedestrians, vehicles, and products on store shelves.<ref>{{Cite book |last1=Adhikari |first1=Bishwo |last2=Huttunen |first2=Heikki |chapter=Iterative Bounding Box Annotation for Object Detection |date=January 2021 |title=2020 25th International Conference on Pattern Recognition (ICPR) |pages=4040–4046 |doi=10.1109/ICPR48806.2021.9412956|arxiv=2007.00961 |isbn=978-1-7281-8808-9 }}</ref>
=== 3D cuboids === 3D cuboid annotation enhances traditional bounding boxes by adding depth, enabling models to predict an object's spatial orientation, movement, and size. This method is particularly useful for autonomous vehicles and robotics, where understanding object dimensions and depth is critical.<ref>{{Cite book |chapter=Annotation tools for computer vision tasks |chapter-url=https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13517/135171A/Annotation-tools-for-computer-vision-tasks/10.1117/12.3055065.short |doi=10.1117/12.3055065 |title=Seventeenth International Conference on Machine Vision (ICMV 2024) |date=2025 |last1=Moschidis |first1=Christos |last2=Vrochidou |first2=Eleni |last3=Papakostas |first3=George A. |page=11 |isbn=978-1-5106-8827-8 |editor-first1=Wolfgang |editor-last1=Osten }}</ref><ref name=kt>{{Cite book |last1=Thakur |first1=Kutub |url=https://books.google.com/books?id=ini3EAAAQBAJ&dq=%22Data+annotation%22+%223D+cuboids%22&pg=PA111 |title=Emerging ICT Technologies and Cybersecurity: From AI and ML to Other Futuristic Technologies |last2=Pathan |first2=Al-Sakib Khan |last3=Ismat |first3=Sadia |date=2023-04-03 |publisher=Springer Nature |isbn=978-3-031-27765-8 |language=en}}</ref>
=== Polygonal annotation === For objects with irregular shapes, such as curved or multi-sided items, polygonal annotation provides more precise labeling than bounding boxes. This technique is often used in applications that require detailed object recognition, such as medical imaging or aerial mapping.<ref name=kt/>
=== Keypoint annotation === Keypoint annotation marks specific points on an object, such as facial landmarks or body joints, to enable tracking and motion analysis. This method is widely used in facial recognition, emotion detection, sports analytics, and augmented reality applications.<ref>{{Citation |last1=Blomqvist |first1=Kenneth |title=3D Annotation Of Arbitrary Objects In The Wild |date=2021-09-15 |arxiv=2109.07165 |last2=Hietala |first2=Julius}}</ref>
==See also== * Reinforcement learning from human feedback * Labeled data * ''Humans in the Loop'' (film)
== References == {{reflist}} {{data}}
Category:Artificial intelligence