Artificial intelligence, especially computer vision has a tremendous impact on our daily lives by greatly enhancing autonomous vehicles, camera tracking, traffic prediction, online fraud detection, and so on. To put it another way, computer vision allows machines to see and interpret their surroundings in the same way that people do.
Yet the performance of your computer vision model evolves around the quality and accuracy of its training data, which is simply made up of annotations of photos, videos, and so on. Today’s article will explain the definition of image annotation, how it processes, and all types of image annotation.
What is Image Annotation?
Image Annotation is the practice of utilizing information from bounding boxes, captions, or keywords, etc to assign meanings to an image or set of images. It often involves human input and, in some circumstances, computer-assisted assistance. This application of Image Annotation techniques is to format or access output images in the database.
Labels are first determined by machine learning engineers to provide information to the computer vision model about the items in the image. The outcome is then utilized to train a model and attain the necessary level of accuracy in computer vision tasks, depending on the quality of your data.
Why is Image Annotation necessary?
Annotations on images are crucial drivers of computer vision algorithms since they provide the training data for supervised learning. Labeling images informs the training model about the image’s significant portions (classes), which it may then use to identify those classes in new images. If the annotations are of good quality, the model will be able to “see” the world and provide correct insights to the application. ML models that are of poor quality will not provide a good representation of relevant real-world items and will perform poorly. Therefore, annotated data is especially useful when the model is attempting to address a problem in a new field or domain.
How does Image Annotation work?
Image annotation can vary in different projects but it usually involves 3 building blocks: a large number of annotators and a suitable annotation platform or tools.
Image annotation projects entail large-scale image annotation by teams of human annotators. The annotators must be well-versed in the project’s needs and capable of performing the necessary annotations accurately. Normally, a human operator will examine a collection of photos, detects relevant things in each image, and annotates the image, for example, by identifying the shape and label of each object. These annotations can be used to generate a dataset for training.
For the tools/platform, you can use any open-source, freeware data annotation tool to annotate objects in the image or find a commercial platform to deploy your project with higher guaranteed quality. It is said that a streamlined and user-friendly annotation tool is at the heart of every successful picture annotation effort. Therefore, it is of utmost importance to find a suitable annotation platform for your projects.
Choosing platform is important to a image annotation project
Typical image annotation processes include the following:
- Prepare the image/video dataset
- Defining the object classes that will be used by annotators to label images
- Execute the labeling tasks according to annotation requirements (drawing around the object to identify things inside each image, classify the images, etc)
- Review the data to ensure correct labeling
- Export the annotated data in a format that can be used as a training dataset(COCO JSON, YOLO, etc.)
What are the different project types of image annotation?
To develop a high-quality dataset for use in computer vision applications, data scientists and ML engineers can choose from a range of annotation types that can be applied to pictures. These are the five most frequent picture annotation kinds in computer vision, which is also five solutions that TagOn provides:
- Image classification
Image classification produces the results of image classification and thereby creating thematic maps. Image classification is divided into 2 types: supervised and unsupervised.
- Object detection
Object detection is a technique to detect objects in technical images and videos. Object detection fields are studied for face detection and motion detection. Object detection has applications in many areas of computer vision, including image retrieval and behavior monitoring.
- Image Segmentation
Image segmentation is the process of segmenting a digital image from complex, many objects into simple image segments, a few objects for easy analysis and change (collection of pixels). Image segmentation is commonly used to format objects and partition different objects.
- Optical Character Recognition (OCR)
Optical character recognition (OCR) is sometimes called text recognition. OCR extracts and repositions data from scanned documents from images. The OCR software points out the letters on the image, joins them into words, and then puts the words into sentences, allowing access to and edit the original content.
- Object Tracking
Object tracking defines objects in videos with high accuracy. it identifies the object and tracks every step of the movement on the screen.
Optical Character Recognition (OCR) – A type of image annotation project
Annotation Tools For Image
ML engineers employ numerous different types of algorithm-based picture annotation approaches. The following are popular image annotation techniques that are used based on the use case. With TagOn’s tools, labeling work becomes easy and efficient, saving working time:
- Rectangles/2D Bounding Boxes
Rectangles/2D Bounding Boxes is an object format in 2 X and Y axes to delimit the object, helping the object to be identified easily. This tool is often used for subjects that do not need too high precision. This tool of TagOn can label images with the highest accuracy, without bias suitable for 2d objects.
- Polygons
Polygons are useful for more precise needs than Rectangles. You can use polygons instead for labeling because polygons can change the vertices of the object making the object labeled more accurately. Polygon is suitable for complex images that need high accuracy, saving time when labeling. Labeled objects will give the most accurate results to apply to AI and machine learning.
- Points
Also called dot/landmark annotation tools. Annotators will place dots or landmarks across the image to identify the objects and their shapes, bigger dots are sometimes used to indicate more important areas. With Points, you can label objects into points to format objects with high accuracy and efficiency and TagOn’s tools will bring out the best experience.
- Key Points
Consists of a series of points (key points), connected by lines. TagOn also provides a pre-developed keypoint frame for common projects such as skeleton, facial expressions, etc
- Polylines
A polyline is a list of points, where line segments are drawn between consecutive points. A polyline has the following properties: Points. The vertices of the line. Line segments are drawn between consecutive points. Polylines are suitable for use on objects such as road markings and footpaths to help annotators accurately identify the object to be labeled.
- 3D Cuboids
The 3D cuboids help to determine the depth of the targeted objects such as vehicles, humans, buildings, etc. 3D shapes help identify objects in 3 axes X, Y, Z and from there the detailed parameters of the object will be shown entirely accurately. Cuboid Annotation is used to simulate 3D images from 2D information. and from the 3D form, we can use it to shape the object. TagOn’s 3D labeling tools will bring users the most efficiency, accuracy, and time and cost savings.
Summary
While the quantity and diversity of your image data is likely to increase on a daily basis, obtaining images annotated to your standards can be a difficulty that hinders your project and, as a result, your time to market. The decisions you make concerning image annotation methodology, annotators, platforms should be carefully considered. TagOn’s Image annotation service helps to provide comprehensive information about an image that can be used to train computer vision models effectively. If you need a professional image annotation solution that provides enterprise capabilities and automated infrastructure, have a look at TagOn.
For more advice, please contact us at the following information:
Contact information:
Website: https://tagon.ai/en
Linkedin: https://www.linkedin.com/company/tagon-data-labeling
Facebook: https://www.facebook.com/TagOnAi/
Phone number: +84 2466 603 178
Email: contact@tagon.ai
