December 1, 2021

Data Labeling: What is Image Annotation?

Data Labeling: What is Image Annotation?

The effectiveness of Artificial Intelligence heavily hinges on the precision of its training data. One pivotal technique for constructing training data in the realm of computer vision is image annotation. This practice is indispensable for enabling Machine Learning algorithms to comprehend objects within their surroundings, effectively teaching them to perceive the world akin to human understanding.

In the context of Machine Learning, annotation encompasses the process of assigning labels to data across diverse mediums, encompassing images, text, and video. Typically, these labels are predefined by a machine learning engineer or a computer vision expert. Their purpose is to furnish the computer vision model with insights into the objects portrayed within an image.

Subsequently, the algorithm leverages the annotated dataset to internalize and discern recurring patterns, which it can then apply when presented with novel and unprocessed data. The choice of annotation modality varies contingent on the project's nature, as different industries demand distinct forms of annotation suited to their respective requirements.

Types of image annotation

Semantic Segmentation

Semantic Segmentation is the task of separating an image into multiple sections and classifying every pixel in each segment to a corresponding class label of what it represents (i.e, pedestrian, car, lamp post). This gives machines a comprehensive understanding of every pixel of a scene in an image.

Semantic Segmentation is commonly used for detection and localisation of a specific object. Applications of such granular understanding of images can usually be found in a variety of industries, and it is especially popular in the Autonomous Vehicle industry, as self driving cars require deep understanding of their surroundings. While in agriculture it is used for analysis of crop fields to detect diseases and abnormal growth.

Bounding Box

The widely utilized form of image annotation is the bounding box. This annotation method involves drawing a box around the primary objects in an image, ensuring it closely aligns with the object's edges. Bounding box annotation is used in a variety of industries for different purposes.

  • Computer Vision and AI Research: Bounding box annotation is a fundamental technique used in computer vision research and AI model training. It forms the basis for object detection, localization, and tracking algorithms.
  • Retail and E-commerce: Bounding boxes are used to annotate products, logos, and objects of interest in images for tasks like inventory management, visual search, and recommendation systems.
  • Automotive and Transportation: Bounding box annotation is crucial for autonomous driving technologies. It helps in identifying pedestrians, vehicles, traffic signs, and other objects on the road.
  • Agriculture: Bounding boxes can be used to mark plants, crops, and pests in agricultural images. This aids in crop health assessment, disease detection, and yield prediction.
  • Healthcare: Medical image analysis often uses bounding boxes to indicate regions of interest, such as tumors or anatomical structures, for diagnostic purposes and treatment planning.
  • Security and Surveillance: Bounding boxes are employed to mark potential threats, individuals of interest, or suspicious objects in surveillance images and videos.
  • Satellite and Remote Sensing: Bounding boxes are used in satellite and aerial imagery to mark geographical features, land use, and changes over time.
  • Manufacturing and Quality Control: Bounding boxes are used to mark defects, components, and product features in manufacturing images to ensure quality control.
  • Fashion and Textile Industry: Bounding boxes help in annotating clothing items, accessories, and design elements for fashion cataloging and e-commerce.

Polygon Annotation

Polygon annotation is a more advanced image annotation technique compared to simple bounding box annotation. Polygon annotation involves outlining the object's shape using a series of interconnected points, forming a polygon that closely follows the object's contours.

Each point in the polygon represents a vertex, and these vertices collectively define the shape of the object being annotated. The use of polygons allows for more precise outlining of irregularly shaped objects and objects with complex geometries, which cannot be accurately represented by a single bounding box.

Polygon annotation is crucial for training AI and machine learning models for tasks like instance segmentation, where the model needs to distinguish between multiple instances of the same object class.

Line Annotation

Line annotation as the name suggests involves the annotation of mainly lines and splines, which are used to draw boundaries in a region of an image. It is primarily used when a section that needs to be delineated is too small or thin and isn’t achievable by bounding box. Line annotation is commonly used to label data for autonomous vehicles.

The lines are used to train vehicle perception models for lane detection. Dissimilar to the bounding box, it avoids white space and additional noise.


Effective data annotation is an iterative process. Continuous monitoring, feedback, and improvement are key to building a high-quality labeled dataset that contributes to the success of your machine learning model.

Get in touch with us to set up a POC or request for a demo of our image annotation tool.