Can Machines Interpret Visual data?

The answer to this question was a ‘no’ until a few years back. 

Now, with the advent of technology like computer vision, machines can process and analyze visuals to interpret and extract insightful data from them.

So, what’s the particular technique that enables computer vision to analyze and understand complex images? 

You guessed it, it’s image segmentation.

So, What is Image Segmentation?

As a subset of computer vision, image segmentation is a process that involves analyzing digital images by breaking them into chunks or segments known as image objects. It divides images at pixel level and groups the image objects in their respective labels (such as man, tree, bus, etc), making it easy to identify them. 

Today, this technique has become indispensable. Its ability to analyze visual data supports a wide spectrum of real-world purposes, including diagnosing diseases and identifying safety issues.

What Does Image Segmentation Do?

Image segmentation helps analyze the results of object detection systems to an in-depth level. 

An object detection software detects objects by drawing a bounding box around them but tells nothing more.

Then an image segmentation explicitly interprets the images to generate insights from the results of object detection. It utilizes learning-based techniques and image processing-based algorithms to help understand the shape of objects, assign labels and classify them according to those labels. 

For example, image segmentation makes it possible to identify the precise location of a building or a vehicle and distinguishes a tree from a man.

Since not all areas in an image need to be processed, image segmentation helps distinguish backgrounds from foregrounds and separates different elements to identify the presence of objects in the images.

This technique is used in medical imaging, robot vision, intelligent video analytics, and autonomous vehicles to equip them with the ability to interpret visual data.

How Does Image Segmentation Work?

In simple words, image segmentation takes images as inputs and provides outputs in the form of a matrix or mask in which every element tells us which class each of them belongs to.

This computer vision process works by dividing images into parts to analyze specific and important areas only. It creates a pixel-wise mask for each object in the image by drawing lines, specifying borders around, and separating important components in an image from the rest of the parts or objects.

By helping label those objects and identify them as a part of a particular class, such as separating humans from objects, it gives us a granular understanding of them. 

Let’s consider a real-world application:

To detect fatal diseases, such as cancer, object detection software is used to locate the presence of the cancerous cells only. 

But, that is not very useful.

However, with image segmentation, the visuals can be divided into pixels and analyzed to get an explicit understanding of the shape of cells, critical for understanding disease and its severity. 

What Are 2 Types Of Image Segmentation?

  • Semantic Segmentation

This type of image segmentation involves detecting a belonging class for every pixel in an image. It associates each pixel with a label or class such as a car, flower, or person. Then, it groups various objects of a similar class, treating them as a single entity, such as identifying a crowd of people in the street as pedestrians.

For example, when the background of an image is segmented as one object and all humans as one object, it is called semantic segmentation. 

Some real-world applications include:

  • Tumor Segmentation
  • Medical Image Segmentation
  • 3D Spatio-temporal Semantic Segmentation


  • Instance Segmentation

Instance image segmentation identifies every element in an image as an individual object. Since it has no clue about what class a particular object belongs to, it treats each object as a distinct entity, unrelated to other parts of the image. 

Application of this type of image segmentation involves tasks such as:

  • 3D instance segmentation
  • Unsupervised object segmentation
  • One-shot instance segmentation
  • Amodal instance segmentation


Real-world tech-supported tasks are increasingly relying on image segmentation. Image segmentation enables machines to process images to such an extent where objects (in the image) can be classified and labeled as humans or vehicles, etc.

This segmentation is the latest advancement that helps draw meaning from images and opens up new possibilities in many industries such as healthcare and transport, to count a few.

It is the high-level precision of image segmentation that can help multiple industries extract insights from the bulk of visual data they have.

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