Image segmentation has changed the way machines perform vision-based tasks. For instance, a few years ago, it was impossible for machines to identify objects and make predictive-based decisions. But the rise and advancements of computer vision have changed the way today’s businesses work to develop computer vision models that can identify objects, recognize their shape, predict the direction of objects’ move-in, and make automated decisions that are best suitable to the given scenario. For example, one of the powerful technologies behind self-driving technology is image segmentation.
Image segmentation is the basis of numerous computer vision tasks. It is the process of segmenting the visual input in order to process it for tasks such as image classification, object detection, and object recognition. Image segmentation methods can be classified into three categories, semantic segmentation, instance segmentation, and panoptic segmentation. However, all the segmentation techniques may not delineate the objects in an image factory with equally satisfying accuracy, which primarily differentiates one technique from the other. One technique may be capable of identifying the presence of different objects in the image, others can be able to separate the occurrences of each object, and some others can perform both tasks with ease.
Types of Image Segmentation
With the growing need for image segmentation, it is important for a user to understand which type of segmentation method best suits their needs. In this article, we shared an overview of three different types of segmentation techniques along with how you can choose the best segmentation technique to practice for different tasks and model building.
Semantic Image Segmentation
Semantic image segmentation is the technique that involves detecting objects within an image and grouping them based on defined categories. Which is simply labeling each pixel of an image with a corresponding class of what is being represented. For instance, you want to categorize different types of flowers based on their color. The semantic segmentation model can be trained to identify the objects ( like followers in an image) based on their color, and later categorize the set of images with the same color flowers into different groups (like images with red followers in group 1, images with blue followers in group 2, images with yellow flowers in group 3, etc.). Because we’re predicting for every pixel in the image, this task is commonly referred to as dense prediction.
Semantic segmentation vs instance and panoptic segmentation
The important factor that differentiates semantic segmentation from the other two image segmentation techniques is that while performing segmentation tasks, we’re not separating the instances of the same class; we only care about the category of each pixel. The segmentation map does not inherently distinguish these as separate segmentation models, which do distinguish between separate objects of the same class. For instance, as mentioned in the above example images with flowers, eventhough we have two different images with flowers of the same class (like a chrysanthemum with two different colors red and yellow) the model is focused to categorize the flower based on the color as it is the definite objective is categorize the same color of objects into one category but not identifying them based on the class of the flower.
Semantic segmentation models are useful for a variety of tasks, including:
- Autonomous vehicles
- Medical image diagnostics
Instance Image Segmentation
Instance segmentation takes semantic segmentation one step further and it involves detecting objects within the defined categories. Unlike semantic segmentation, instance segmentation consists of the localization of specific objects based on the association of their belonging pixels. Which makes it more challenging to develop. The requirement of the prediction of object instances and their per-pixel segmentation mask. For instance, the instance image segmentation method can be leveraged to identify defined objects (let’s consider our aim is to identify balloons in a given image), the model will not only recognize all the balloons but also help us to distinguish them separately. Unlike semantic segmentation, all the balloons have different colors or labels, as semantic segmentation doesn’t recognize more of the same objects in one image as different.
Instance segmentation vs panoptic and semantic segmentation
Instance segmentation is a natural sequence of semantic segmentation, and one of the challenging models to work with when compared to other segmentation techniques. As the goal of instance segmentation is to get a view of objects of the same class divided into different instances, automating this process is not an easy task. State-of-the-art instance tagging provides the user with additional information about the data and helps in making effective decisions in unknown situations.
Instance segmentation models are useful for a variety of tasks, including:
- Marine surveillance to protect against illegal fisheries, oil discharge control, and sea pollution monitoring.
- Retail for operations optimization, security, and surveillance.
Panoptic Image Segmentation
Panoptic segmentation semantically distinguishes different objects as well as identifies separate instances of each kind of object. In other words, panoptic segmentation assigns two labels to each of the pixels of an image – 1. Semantic label, 2. Instance ID. The pixels having the same label are considered to belong to the same semantic class and the instances IDs differentiate its instances. Unlike instance segmentation, each pixel in panoptic segmentation has a unique label corresponding to the instance which avoids misinterpretation of information.
Another important detail to understanding panoptic segmentation is to understand two important terminologies germane to image segmentation:
- Things: any countable object is referred to as a thing in computer vision terminology. Examples include persons, animals, flowers, vehicles, etc.
- Stuff: uncountable amorphous region of identical texture is known as stuff. For example, road, rail line, water, sky, and more.
The study of things falls under the instance segmentation category and the study of stuff fall under semantic segmentation.
Panoptic segmentation vs instance and semantic segmentation
While previous image segmentation techniques we have focused on using end-to-end trainable architecture to identify and differentiate objects in an image. As panoptic segmentation leverages the complementarity between the semantic and instance segmentation tasks to improve both accuracy and performance, the model needs to be trained with distinct algorithms with huge volumes of data. For example, below we have a picture of kitties.
While performing panoptic segmentation, the model classifies all the pixels in the image as belonging to the class label and it also identifies what instance of the class they belong to. In the below image, we can see the differentiation of stuff and things mentioned above, along with all the cats identified with different IDs which allows us to identify the region of each cat and differentiate one from the other. As well, stuff regions ( such as background and pavement) are identified as semantic segments without instances IDs.
Panoptic segmentation models are useful for a variety of tasks, the application of this technique ranges from medical image analysis for early detection of cancer, digital image processing, self-driving cars to understand what surface the vehicle is driving on, and switch between driving modes, etc.
So far, we have discussed the major differences between three major types of image segmentation: semantic, instance, and panoptic. Our article also provides an overview of each image segmentation technique and how they can be leveraged at a business advantage. To know more about image segmentation techniques and more about other types of computer vision applications connect with soulpage expertise.