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Semantic vs Instance vs Panoptic: Which Image Segmentation Technique To Choose?

Semantic vs Instance vs Panoptic: Which Image Segmentation Technique To Choose?
Semantic vs Instance vs Panoptic: Which Image Segmentation Technique To Choose?

The way machines carry out vision-based activities has altered as a result of image segmentation. For instance, just a few decades ago, object identification and prediction-based decision-making were difficult for machines.

However, the development of computer vision models which can identify objects, recognize their shapes, forecast the direction in which objects will travel, and make automatic decisions that are most appropriate for the given circumstance has altered how organizations operate today.

For instance, image segmentation is one of the potent technologies used in self-driving technology.

Many computer vision tasks start with image segmentation. To process the visual input for tasks like image categorization, object identification, and object recognition, it is necessary to segment the visual input.

Semantic, instance, and panoptic segmentation are the three categories into which image segmentation techniques can be divided.

The primary difference between one segmentation technique and another is that not all segmentation techniques will accurately define the items in an image factory.

One technique might be able to tell which things are there in the image, another might be able to tell where each object appears, and still, another might be able to do both with ease.

Image segmentation types

Given the increasing demand for image segmentation, users must know which kind of segmentation technique effectively meets their requirements. In this post, we gave an overview of three distinct kinds of segmentation techniques and discussed how to pick the most appropriate one to use for model development and various tasks.

Semantic segmentation

The process of semantic image segmentation involves finding objects inside an image and categorizing them according to predetermined categories. This entails merely assigning a class name to each pixel in an image that corresponds to the thing it is meant to represent.

For instance, you might want to group several flower varieties according to their hue. The semantic segmentation model may be trained to recognize things in an image (like followers) based on their color, and can then classify a collection of photos of flowers with the same hue into several categories (like images with red followers in group 1, images with blue followers in group 2, images with yellow flowers in group 3, etc.).

Semantic segmentation

Example of Semantic segmentation

Instance Segmentation

By finding things that fall within the specified categories, instance segmentation advances semantic segmentation. In contrast to semantic segmentation, instance segmentation involves localizing particular objects based on the relationship of the pixels that correspond to them. It makes development more difficult. the need for per-pixel segmentation masks and object instance prediction.

If our objective is to locate balloons in a given image, for example, we may use the instance image segmentation approach to identify those items. The model will not only identify the balloons, but it will also help us distinguish them from one another.

Since semantic segmentation doesn't distinguish between more of the same things in a single image as distinct, all of the balloons have various shades or labels.

Instance Segmentation

Example of Instance Segmentation

Panoptic Segmentation

Semantically differentiating various objects by panoptic segmentation, which also detects distinct instances of each type of item. In other words, panoptic segmentation gives each pixel in an image two labels: a semantic label and an instance ID.

The instances IDs distinguish its instances, whereas the pixels with the same label are thought to belong to the same semantic class. In contrast to instance segmentation, panoptic segmentation assigns a distinct label to each pixel that corresponds to an individual instance to prevent information from being misinterpreted.

Panoptic Segmentation

Example of Panoptic Segmentation

Applications in the Real World

There are overlapping uses for all 3 image segmentation methods in image processing and computer vision. Together, they have numerous practical uses that aid in expanding humankind's cognitive capacity. Semantic and instance segmentation has a variety of practical uses, including:

  • Autonomous vehicles, often known as self-driving automobiles, can better understand their surroundings by distinguishing various objects on the road thanks to 3D semantic segmentation. Instance segmentation recognizes each object instance at the same time to provide speed and distance calculations more depth.

Autonomous vehicles

Autonomous vehicles

  • MRI, CT, and X-ray scan analysis: Both methods can find cancers as well as other abnormalities in these types of images.

MRI, CT, and X-ray scan analysis

Brain Imaging: MRI, CT, and X-ray scan analysis

  • The world can be mapped from space or an altitude using either satellite or aerial images. They can draw the contours of various natural features, including mountains, deserts, rivers, and buildings. Their use in scene comprehension is comparable to this.

Differences between Semantic vs Instance vs Panoptic Segmentation

Every pixel in an image is assigned a class label using semantic segmentation, such as a human, flower, car, etc. Multiple objects belonging to the same class are considered to be one entity. Comparatively speaking, instance segmentation treats several objects belonging to the same class as unique individual instances.

To combine the ideas of semantic and instance segmentation, panoptic segmentation gives each pixel in an image two labels: (i) a semantic label, and (ii) an instance id.

The similarly marked pixels are regarded as being members of the same semantic class, and their instances are identified by their unique identifiers (ids).

1. Panoptic segmentation and Semantic segmentation

Each pixel in an image must be given a semantic label for both semantic and panoptic segmentation tasks. Therefore, if the data point does not define instances or if each of the classes is the thing, both strategies are equivalent. These tasks are distinguished by the addition of item classes, each of which may include many instances per image.

2. Instance Segmentation and Panoptic segmentation

Each instance of an object in an image is segmented using both instance segmentation and panoptic segmentation. But how overlapping parts are handled makes a difference. Although the panoptic segmentation task provides for the assignment of a distinct semantic label and a distinct instance-id to each pixel of the picture, instance segmentation allows for the overlap of segments. As a result, there can be no segment overlaps in panoptic segmentation.

3. Confidence levels

Semantic segmentation and panoptic segmentation don't need confidence scores for each segment, in contrast to instance segmentation. This makes it simpler for these techniques to investigate human constancy. However, segmentation is a challenging study because human annotators do not directly provide confidence scores.

4. Evaluation metrics

IoU, pixel-level accuracy, and mean accuracy are often used metrics for semantic segmentation. These measurements only take into account labels at the pixel level and neglect labels at the object level.

These metrics cannot assess thing classes because instance identifiers are not taken into account.

For example, AP (Average Precision) is used as the benchmark statistic for segmentation. For the computation of a precision/recall curve, each segment must have a confidence score assigned to it. The result of semantic segmentation cannot be measured by confidence scores or AP.

Instead, PQ (Panoptic Quality), a measurement for panoptic segmentation, treats all classes equally, whether they are things or junk. PQ is not an amalgam of semantic and instance segmentation metrics, it must be made clear.

For each class, the segmentation and recognition quality indices SQ (i.e. average IoU of paired segments) and RQ (i.e. F1-Score) are computed. The formula for PQ is then (PQ = SQ * RQ). As a result, it harmonizes evaluation across all classes.

Partner with Labellerr to Fasten Your Segmentation Process

Labellerr is an effective tool for image segmentation since it offers a complete solution to collect, curate and annotate data. Users of Labellerr can quickly compile a significant number of varied photos pertinent to their segmentation work.

The platform provides a streamlined procedure for curating and organizing the gathered data, ensuring it is properly managed and easy to find. Thanks to Labellerr's powerful annotation capabilities, users can annotate images with exact segmentation borders, precisely defining the regions of interest.


Generally speaking, the specific needs of your application will choose which image segmentation technique to apply. Semantic segmentation may be the best option if you need to categorize pixels into predetermined types.

Instance segmentation might be a preferable option if you need to locate specific instances of each class inside a picture. Panoptic segmentation can be the best choice if you need to achieve both of these things.

In short, image segmentation has drastically changed machines’ visual abilities, and in turn their decision-making process. This technology is still being developed and improved upon, with new applications for it being discovered all the time.

As machine learning and artificial intelligence continue to evolve, likely, image segmentation will too, open up even more possibilities for the future.

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