Explain Image Segmentation in detail?

Image segmentation is a commonly used technique in digital image processing and analysis to partition an image into multiple parts or regions, often based on the characteristics of the pixels in the image.

With image segmentation, each annotated pixel in an image belongs to a single class. It is often used to label images for applications that require high accuracy and is manually intensive because it requires pixel-level accuracy. A single image can take up to 30 minutes or beyond to complete. The output is a mask that outlines the shape of the object in the image. Although segmentation annotations come in a lot of different types (such as semantic segmentation, instance segmentation, panoptic segmentation, etc), the practice of image segmentation generally describes the need to annotate every pixel of the image with a class.

What are the benefits of using image segmentation for my ML model?

The primary benefit of image segmentation can be best understood by comparing the three common annotation types within computer vision: 1) classification 2) object detection and 3) image segmentation.

  • With image classification, the goal is to simply identify which objects and other properties exist in an image.
  • With image object detection, you go one step further to find the position (bounding boxes) of individual objects.
  • With image segmentation, the goal is to recognize and understand what’s in the image at the pixel level. Every pixel in an image belongs to a single class, as opposed to object detection where the bounding boxes of objects can overlap.