How to label images for semantic segmentation

Natalie Kudan

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Semantic image segmentation is the process of separating sections of an image into clusters of pixels relating to corresponding objects, with its classification.

The major purpose of segmentation entails assigning an appropriate label to each pixel depending on what that pixel represents in the picture. Thus, it divides images into multiple zones or segments in accordance with the object to which pixels belong. To put it another way, segmentation is done based on the characteristics of the pixels that indicate objects or boundaries, so that the image can be simplified and analyzed in a more efficient way. That way we get a pixel-by-pixel mesh of the object.

As opposed to the detection of objects on a picture, the objective of semantic segmentation is more complicated. The reason for this is the requirement to correctly determine whether an object in the image belongs to a distinct category, as well as to define the boundaries and composition of the object in the image in the most precise way possible.

Complex computer vision tasks, such as object detection and location, are achievable by training machine learning models with data labeled via semantic segmentation. In order to construct a machine learning model for semantic segmentation, labels must be assigned to the dataset on the pixel level. Semantic segmentation is visualizing multiple objects of the same class as a unified entity which is mainly applied to teach a natural environment object perception model.

Segmentation varieties

Besides semantic segmentation, which allows to determine whether collections of pixels in an image are assigned to particular categories of objects, there are two types of segmentation:

  • Instance segmentation extracts each object within a certain class into separate segments, contrary to a semantic one;
  • Panoptic segmentation joins the tasks of semantic and instance segmentation. Additionally, every pixel in the panoptic segmentation task must contain precisely one label.

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What is semantic segmentation used for?

As mentioned before, the purpose of using semantic segmentation lies in assigning sets of pixels in an image to certain classes of objects in order to accurately classify and segment each object represented in an image. At present, semantic segmentation is one of the crucial tasks in the computer vision domain. After processing all items in an image, the program is able to learn to properly comprehend the observed scene. The method allows AI-powered systems to be able to "see", i.e. gather accurate information of their surroundings, and thereby help and makes people's lives easier.

Applications of semantic image segmentation

Semantic segmentation has applications in various computer vision systems, for instance, autonomous cars and robots. These systems are designed to accurately capture visual information about the surroundings, while a critical prerequisite for them to function properly involves an algorithm's ability to interpret the device's current surroundings.

The fashion industry employs the process to retrieve apparel elements from an image to propose similar in-store merchandise. More advanced engines are able to re-dress individuals directly in the image.

Yet another major area of application is healthcare, a field where semantic or instance segmentation may be utilized to evaluate x-ray and EM images, such as in dental medicine, pulmonology, oncology, genetics, and so forth. Satellite images and maps, crowds of people, and deep-space objects are examined via segmentation.

A few other applications of image segmentation are:

  • Retail and e-commerce (product recognition on store shelves, virtual fitting rooms, people counting for retail stores)
  • Transportation (pedestrian detection, traffic prediction, parking occupancy detection, road condition monitoring)
  • Manufacturing (personal protective equipment detection)
  • Biometrics (facial feature detection, iris recognition)
  • Agriculture (plant disease detection, object detection in agriculture)
  • Marketing (logo recognition)

Semantic segmentation annotation in a nutshell

Semantic segmentation involves taking three major steps:

  1. First of all, all categories presented in the processed image are distinguished.
  2. Identification of an object is performed next. Both the essence of the classes and the information regarding the positional arrangement of these classes are determined.
  3. The final step involves actual segmentation. Labels are created for each pixel, showing that a particular pixel is a member of a defined class. As a result, a segmentation mask is generated.

Semantic segmentation approaches

Multiple algorithms and methods have been elaborated for image segmentation. However, no single solution for the task at hand exists, and frequently techniques have to be combined to efficiently handle the job.

Interactive image segmentation

Being one of the image annotation techniques, image segmentation stands among the most highly valued and time-consuming modes of annotation. Manual annotation has enabled the collection of datasets that form the ground of all modern computer vision innovations. What distinguishes interactive segmentation from the other methods is the fact that it involves human assistance throughout the entire process. A person may highlight the desired items with bounding boxes or a polygon tool, and adjust the algorithm's faults.

Interactive segmentation may be applied in a multitude of applications as well as for image editing. Manual segmentation is a lengthy and labor-intensive procedure. Crowdsourcing helps solve many problems related to collecting training data on a larger scale.

Grayscale-based segmentation

This is the simplest approach to semantic segmentation, an annotation method which consists of manual encoding of rules or features a region must conform to in order to be assigned a particular label. Such rules may be expressed as pixel characteristics, for instance, gray color intensity.

Markov Random Fields

The method of Markov Random Fields (MRF) or Conditional Random Fields (CRF) is widely applied in semantic image segmentation on account of its excellent capacity for dimensional characterization and describing relations.

MRFs are a category of statistical modeling techniques, employed for structural forecasting. Prior to any prediction, they may consider the adjacent environment, such as the relationship between pixels. Thus making it an ideal choice to implement semantic segmentation. Nevertheless, certain objects are rather tiny and unevenly dispersed throughout the pictures, making it extremely easy to incorrectly classify such pixels into different classes.

Deep Learning Methods

As computing capacity increases, deep neural network-based algorithms are gaining popularity. Deep learning has significantly simplified the semantic segmentation workflow model, while simultaneously demonstrating remarkable quality.

The majority of popular image segmentation deep learning algorithms employed employ convolutional layers in their design. Among them, the Fully Convolutional Network (FCN) represents one of the easiest and most common designs employed for semantic segmentation. It reduces network parameters significantly, as well as allows achieving relative stability to transfer, scaling, and insignificant distortions in the images.

Wrapping up

The semantic segmentation data labeling helps improve the accuracy of computer vision models. Gathering new datasets which contain precise information about certain types of objects is particularly important for model training when models are supposed to identify images in new domains.

Toloka provides an environment where such datasets can be gathered: a data labeling platform where millions of people from all around the world perform labeling tasks posted by AI teams. The platform brings the two audiences together, and provides smart technologies which help transform the crowd into computing power.

We invite you to browse through our website and some of our step-by-step instructions to learn more:

Article written by:
Natalie Kudan

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