In This Article

Do you find self-driving cars interesting? Or virtual mirrors? Or maybe your camera filters? Well, all of them are just examples of computer vision, the new face of the modern era of technology. The technical process behind computer vision is semantic segmentation by which images get recognized.

The main function of image segmentation is to train Machine Learning (ML) models to develop computer vision. With the help of image segmentation and annotation, the ML algorithm will develop the intelligence to detect things. In recent times, the development of machine learning models for computer vision has been very rapid. Big companies are using these models to develop automation or automation-enabled tools.

However, image annotation through segmentation plays a key role in developing these models. Essentially, to develop computer vision, you need to have a high-quality image database. On the other hand, to annotate data, you have to rely on reliable annotation tools with human intelligence.

Let’s discuss more about image segmentation in this blog in detail. We’ll aim to discover the application of image segmentation in various fields here.

A. What is Semantic Segmentation

Image segmentation is a part of the visual annotation projects that helps to classify objects through images. The image classifiers often rely on bounding boxes to detect objects and classify them. Computer vision developers prefer to annotate images through a semantic classification process. Because this semantic segmentation technique can provide pixel-perfect accuracy and export fine information from image content.

Instead of drawing boxes around the objects, this technique defines the exact boundary of the objects. It’s more like paper cutouts that exactly cut images around the border. However, digital images can have multiple segments that can be classified into different regions. With the help of image segmentation, classifiers name each of the regions.

The process of semantic segmentation helps label each pixel that an image can contain. With the advancement of Artificial Intelligence (AI), computer vision emerged as a subset of AI that needs visual data. Here, semantic image segmentation helps AI models with visual data in building computer vision.

Consider the semantic segmentation deep learning algorithm that connects every pixel with a label. Therefore, a collection of pixels will create a distinct category and help AI models to recognize objects. For instance, in a crowded street, computer vision can identify different vehicles, traffic signs, pedestrians, bridges, and other things.

Besides developing autonomous cars, image segmentation techniques are getting deployed in the medical field as well. One interesting fact about semantic image segmentation is that it can categorize each pixel from an image. For example, semantic image segmentation will categorize a person standing on a road into multiple pixes. This includes the person, cars, buildings, roads, light posts, and other elements that you can see on the road.

B. Semantic Segmentation vs. Instance Segmentation

Image classifiers can perform image segmentation in three different ways, instance, semantic, and panoptic. Each approach is different in terms of the semantic segmentation annotation process and hence image classifiers choose that suits the project.

Semantic segmentation and instance segmentation are techniques used in computer vision to understand and interpret images, particularly in the context of object recognition.

Semantic image segmentation involves grouping each pixel in an image under a specific label. Imagine an image with a car – every pixel belonging to the car would be categorized under the same label, typically denoted as “car.” This method allows the system to understand the composition of the image at a high level, recognizing different objects and their boundaries.

Now, instance segmentation takes semantic image segmentation further by providing a more detailed analysis. While semantic image segmentation in action groups all car pixels under the generic “car” label, instance segmentation goes on to distinguish between individual objects of the same category. Therefore, to know what is semantic segmentation, you have to understand everything in detail.

In the case of multiple cars in an image, each car would be assigned the “car” label but given different colors or identifiers. This means that the system not only recognizes that there are cars in the image but can also differentiate between them at a granular level. For this, many computer vision developer companies rely on outsourcing Semantic Image Segmentation Services for their benefits.

  • An Illustration

To illustrate, consider an image with three cars. Semantic segmentation would group all the pixels of these cars under the common label “car.” On the other hand, instance segmentation would assign unique colors to each car, allowing the system to identify and differentiate them individually while still categorizing them under the broader “car” label.

The significance of instance segmentation lies in its ability to provide a more nuanced understanding of the visual content. This refined segmentation is particularly valuable in applications where distinguishing between specific instances of an object is crucial. For example, in autonomous driving systems, it’s not just about recognizing that there are cars on the road (semantic image segmentation) but also precisely identifying each car to navigate through traffic effectively.

Both semantic and instance segmentation play essential roles in advancing computer vision capabilities. Semantic image segmentation provides a foundational understanding of an image, while instance segmentation adds a layer of detail by recognizing and distinguishing between individual instances within the recognized categories. These semantic segmentation techniques collectively contribute to the development of more sophisticated and accurate computer vision systems with applications across various industries.

After discussing semantic segmentation vs. instance segmentation, now move into how this segmentation system works.

C. Process of Labeling Images for Semantic Image Segmentation

Semantic segmentation is a computer vision technique that generates a segmentation map for an input image. In this map, each pixel is color-coded based on its semantic class, creating distinct segmentation masks.

These masks represent specific portions of the image, allowing the model to differentiate between various elements. For instance, in an image featuring a tree in an empty field, the segmentation map might include masks for the tree, the ground, and the sky in the background.

To achieve this, semantic image segmentation models leverage complex neural networks. These networks work to accurately group related pixels into segmentation masks while identifying the real-world semantic class for each pixel group or segment. The process involves deep learning methods, requiring the model to undergo training on extensive pre-labeled datasets annotated by human experts.

  • Model adjustment

During training, the model adjusts its weights and biases through machine learning techniques like backpropagation and gradient descent. This allows the model to learn patterns and associations between pixels and their semantic classes. Deep learning methods, including semantic segmentation, have largely replaced traditional machine learning algorithms like Support Vector Machines (SVM) and Random Forest.

Despite the increased time, data, and computational resources needed for training, deep neural networks have proven more effective, leading to their widespread adoption. Early successes in improving accuracy and performance established deep learning as the preferred approach in computer vision tasks. With the help of semantic segmentation annotation, computer vision models are getting more accurate.

  • Insights into image content

Semantic image segmentation’s significance lies in its ability to provide detailed insights into image content. The model can discern and categorize different elements within an image by color-coding pixels based on their semantic classes. This capability has applications in various fields, from autonomous vehicles navigating complex environments to medical image analysis for identifying specific structures.

Semantic image segmentation employs neural networks to create segmentation maps, offering a pixel-level understanding of image content. This method’s reliance on semantic segmentation deep learning has propelled it to prominence, outperforming traditional machine learning algorithms and becoming a cornerstone in advancing computer vision applications.

D. The Application of Semantic Segmentation

I. Facial Recognition

Semantic image segmentation in facial recognition dissects faces into categories like eyes, nose, mouth, skin, hair, and background. This detailed segmentation allows computer vision systems to discern and analyze specific facial features.

By training on this data, applications gain the ability to distinguish an individual’s ethnicity, age, and expression. This method enhances the precision of facial recognition systems, enabling them to capture nuanced details. Also, it makes more refined assessments of the characteristics and emotions expressed in a person’s face.

The semantic segmentation of facial features provides a granular understanding and empowers computer vision technologies to navigate complex facial variations. Therefore, it contributes to more accurate and sophisticated facial recognition applications.

II. Virtual Fitting Rooms

Nowadays, virtual fitting rooms are becoming a trend, and clothing companies are focusing on that. Without changing clothes, people can get a virtual representation of them wearing the same clothes. Therefore, this system fastens up the cloth selection process and saves time consumed in trial rooms.

A virtual fitting room must have a virtual mirror where customers would get their images reflected. These mirrors provide partially reflective and partially transmissive images of customers. Hence, you can try different clothes without even changing clothes multiple times in trial rooms.

III. Self-Driving Vehicles

Up until now, self-driving cars are the most complex practical application of computer vision. These computer vision models guide the engine on how to drive on the road. The application of semantic segmentation is huge here in developing computer vision models. With the application of semantic image segmentation, these models get the highest form of accuracy.

Imagine poorly trained computer vision algorithms, it will pose a threat to the entire system. They can endanger common pedestrians on the road as well as vehicles. Here, semantic image segmentation in action helps companies to put detailed instructions of an image pixel to pixel accurately. Therefore, computer vision algorithms can detect small details like lenses, traffic signs, etc accurately.

IV. Medical Imaging and Diagnostics

Machine learning and image segmentation play a significant role in detecting clinical difficulties in the human body. Through the scanned images generated from CT scans or MRI, these computer vision models can detect abnormalities. Moreover, doctors can rely on these models as they bring efficient results in medical treatment.

E. Can You Consider Outsourcing?

Of course yes! If you are into developing computer vision models then outsourcing image segmentation will suit you best. Outsourcing companies generally rely on cutting-edge tools to segment images. With the best effort of human annotators, outsourcers would make the semantic segmentation process better.

Therefore, trusting an outsourcing company will ensure high-quality training data for your computer vision models. These companies will focus on your personal recommendations while doing semantic segmentation annotation.

Tell us your Requirements & Speak to our Experts

We are always ready to help you!

ASK Data Entry has over a decade of outsourcing experience providing a range of data entry solutions to clients worldwide. Our team brings the highest quality and accuracy to every project, while ensuring confidentiality and compliance with global outsourcing best practices.

Start With Our FREE TRIAL

Add notice about your Privacy Policy here.