Understanding Semantic Segmentation Vs. Instance Segmentation for Object Recognition

December 9, 2022

| Carolyn Joy V.

Artificial Intelligence (AI) can only advance as well and as quickly as the data that it trains with. This makes the process of data labeling one of the more crucial parts of the development of machine learning and deep learning algorithms. With high quality data and accurate labels, an AI model has a greater chance of learning and accomplishing the goal it’s been set out to do.

All that said, the approach to data labeling may vary significantly. Some algorithms can work with scant details in the label, while others may call for more refinement — identifying medical imagery for instance, and these projects require more than just the use of bounding boxes that’s common for basic image localization. For complex models, datasets need to undergo an essential task in computer vision called image segmentation.

What is Image Segmentation?

Image segmentation is the process of classifying parts of an image together according to the same object class. It involves dividing the digital image (or video frames) into image segments, effectively reducing its complexity and allowing for further analysis of each segment. Segmentation includes separating foreground from background, as well as grouping regions of pixels based on similarities in shape and color; hence it is also called pixel-level classification.

Image segmentation is one of the key processes at work in object detection. When presented with a complete image, a segmentation algorithm first finds objects of interest within that image, and then the object detector proceeds to zero in and analyze what’s already been defined by the bounding box via the algorithm. Using segmentation, there’s no need to process the entire image, and focus is instead placed on the important segments or objects.

Key Differences: Semantic Segmentation Vs. Instance Segmentation

Object recognition for computer vision projects are generally based on two types of image segmentation techniques: semantic segmentation and instance segmentation.

Semantic segmentation is labeling each pixel of an image according to a predefined class that is being represented, and separating it from other images classes by overlaying it with a segmentation mask. It doesn’t attempt to differentiate across different instances of the same object class. So if a photo is taken of a bustling city street for example, the image can be segmented into vehicles, pedestrians, buildings, sidewalks, and so on. All objects of the same class are assigned the same pixel or color.

Semantic segmentation

On the other hand, instance segmentation is used when the task is to give a unique label to every instance of a particular object in the image. This means that instance segmentation takes semantic segmentation a step further because it just doesn’t stop with identifying the different image classes; it distinguishes objects of the same class and treats each as a distinct entity. Going back to the previous example of a city scene, an instance segmentation model would be able to identify each instance of a car or person, and assign a unique label or color to each.

Instance segmentation

Use Cases: Semantic Segmentation Vs Instance Segmentation

The level of detail needed in labeling datasets depends primarily on the project, and this is where you will have to determine whether semantic segmentation will suffice or if you will need the higher accuracy of instance segmentation.

For a good number of computer vision tasks however, deep learning-based semantic segmentation models have proven to be successful. These models are commonly used for:

When a higher level detail is required for any of these tasks, then an instance segmentation model is applicable. In the medical domain for example, the accuracy of instance segmentation is particularly useful for detecting and segmenting tumors in MRI brain scans.

Data Labeling and Image Segmentation for the Future

As computer vision projects advance, the need for image detection algorithms that provide a high level of accuracy also increases. These help produce high quality and accurate data sets that establish ground truth quickly, hasten the workflow, and ensure the project’s success.

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