What is image segmentation?

Image segmentation is a computer vision approach that splits an image into several segments. In most cases, a separate visual feature or item is associated with each of the picture’s discrete segments.

Its purpose is to detect and separate the distinct objects or areas in a picture to allow additional analysis or processing. Object identification, picture classification, and image retrieval are just a few examples of the numerous computer vision applications that rely on image segmentation.

How are images annotated?

To annotate a picture, one must normally locate and name the features of interest. There are various distinct forms of picture annotation, including bounding box annotation, polygon annotation, semantic segmentation, and instance segmentation.

Different types of image annotation

  • Bounding box – An image’s subject may be highlighted with a rectangle using this annotation technique. Annotations of this kind are often used in applications requiring the identification and location of objects.
  • Polygon annotating entails drawing a closed polygon around the item of interest in a picture. This form of annotation is widely used for object segmentation and instance segmentation tasks.
  • Semantic segmentation demands classifying each pixel in a picture with a matching class label. Tasks involving the categorization of images and the identification of objects often make use of this form of annotation.
  • Instance segmentation includes assigning a class label and an instance ID to every pixel in a picture. Activities involving object segmentation and tracking often use this kind of annotation.

Image segmentation dataset

Images and their associated segmentation labels are the standard components of an image dataset. Models of segmentation are trained and tested on these datasets. Popular examples of image segmentation datasets include the Pascal VOC dataset, the MS COCO dataset, and the Cityscapes dataset.

  • Image object segmentation refers to the process of dividing a picture into distinct visual elements. The purpose of image object segmentation is to distinguish various items in a picture and assign each object to a different segment. This job is often utilized in object detection and tracking applications.

Different types of Image Segmentation tasks

  • Binary – Separating a picture into its two main categories, foreground, and background, is the goal of binary segmentation. This is a typical step in many picture editing and background removal programs.
  • Multi-class – When a segmented image is divided into many classes, or areas, this is called multi-class segmentation. This job is often used in medical imaging applications to separate distinct tissues and organs.
  • Instance segmentation – includes segmenting a picture into various instances of the single class. This job is often used in robotics and autonomous driving applications to monitor and recognize distinct objects in the surroundings.

Typical Methods for Segmenting Images

  • Thresholding is the process of segmenting a picture into foreground and background at a predetermined value. Although straightforward and effective, this method may struggle with complicated photos that have variable illumination.
  • Edge detection includes recognizing edges in a picture based on changes in intensity or color. This method works well for photographs with clean, distinct edges, but it may struggle with more intricate textures and structures.
  • Region growth involves repeatedly grouping pixels based on their similarity in color or texture. This method works well in photos when the areas to be divided up are all similar in appearance.
  • Clustering– Pixels are grouped using methods like k-means and hierarchical clustering to form groups with shared visual characteristics. To get optimal results, this method may need substantial parameter adjusting, yet it is effective for segmenting pictures with complex textures and structures.

Application of Image Segmentation

Uses of segmentation images include object identification, image classification, image retrieval, medical imaging, autonomous driving, robotics, and surveillance. Segmenting pictures is a cornerstone procedure in mentioned areas since it paves the way for additional analysis and processing.

For example, it is used in medicine to facilitate diagnosis and therapy planning. Additionally, it’s utilized in remote sensing to categorize different forms of land cover, such as forests and bodies of water. In the object detection area, it is employed to properly pinpoint items in an image. Lastly, in image recognition, it is used to extract characteristics from a picture that may be used to categorize it into distinct groups.


Image segmentation is a key problem in computer vision that includes splitting an image into parts that match certain objects or locations. In order to annotate a picture, it is necessary to draw lines around the edges of objects and areas by hand.

An advanced computer vision picture annotation technique is used, for instance, in a self-driving car. This model identifies and classifies everything around the automobile, including other vehicles, people, bicycles, trees, and more. The computer in the car uses this information to guide it through traffic efficiently and safely.