In the realm of AI and Computer Vision, annotated images play a vital role in training algorithms. Among various image processing techniques, image segmentation stands out as a powerful technique that enables machine learning models to comprehend and interpret complex visual data with high precision. To achieve this capability, image segmentation is further categorized into three specific types: semantic segmentation, instance segmentation, and panoptic segmentation, each tailored to specific data applications. This article delves into these three image segmentation techniques’ definitions, benefits, key differences, and applications.
What is Image Segmentation?
Image segmentation is a computer vision technique that divides an image into multiple segments or regions based on certain characteristics, such as color, intensity, texture, or motion. The goal of image segmentation is to simplify the representation of an image by partitioning it into meaningful parts, making it easier to analyze and understand. As a result, the output of the image segmentation task is a new image, often called a segmentation mask. Imagine you have a picture with a cat and a dog. Image segmentation would identify the individual cat and dog as separate objects, rather than just processing the entire image.
There are various applications for types of image segmentation. For instance, it’s used in self-driving cars to distinguish between lanes, vehicles, and pedestrians.
Read more: What is Data Annotation?
Exploring Types of Image Segmentation
Image segmentation tasks can be classified into three sub-categories:
- Semantic segmentation
- Instance segmentation
- Panoptic segmentation
Check the types of image segmentation in detail.
| Semantic segmentation | Instance segmentation | Panoptic Segmentation | |
| Description | Assigns a class label to each pixel in an image, grouping similar pixels based on their semantic meaning. | Not only labels pixels with classes but also differentiates objects of the same class. | Aims to merge the advantages of semantic and instance segmentation. This approach bridges the gap between understanding scene semantics and recognizing individual objects, making it valuable for tasks requiring holistic scene understanding. |
| Result | Each pixel is assigned to a specific class. | Offer pixel-level segmentation with distinct labels for each instance of a class. | The image is segmented into coherent semantic regions and individual instances in a class. |
| Cost | Requires the least computational power as it only needs to classify each pixel into one category. | Requires more processing power to distinguish individual objects. | It is the most complex and demands the highest computational resources. |
| Targeted projects | Semantic segmentation primarily targets projects requiring scene understanding, object classification, and image annotation. This includes tasks such as scene parsing, semantic image labeling, and image classification based on semantic content. | Instance segmentation is tailored for projects demanding precise object delineation and instance-level analysis. This encompasses tasks such as object detection, instance counting, object tracking, and semantic instance segmentation where objects of the same class are distinguished at the instance level. | Panoptic segmentation is designed for projects that necessitate holistic scene understanding, simultaneous object detection, and semantic analysis. Avoids the disadvantages of semantic segmentation and instance segmentation. Therefore, it serves as a data annotation method for projects requiring high precision accuracy up to 100%. Panoptic segmentation is widely used in autonomous vehicle projects to perceive the surrounding environment and make quick and safe decisions. |
| Example | Using semantic segmentation in an image of a street with trees and vehicles, this technique will annotate all types of trees into one class (tree) and all types of vehicles (like cars, motorbikes, buses) into one class (vehicle) | Instance segmentation helps us define different instances in a class like numerous cars in a street photo. | Combining the segmentation of specific classes (roads, sidewalks, trees) with specific instances in a class (cars, pedestrians). |
| Limitations | This approach does not provide in-depth detail into complex images in some specific requirements. | Instance segmentation may struggle with accurately segmenting objects that are occluded or overlapping with each other. | Requires extensive labeling efforts for both semantics and instances. |
| Some common techniques | – Fully Convolutional Networks (FCN) – U-Net – DeepLab | – Mask R-CNN – FCN with object detection heads | – Panoptic FPN – Panoptic-DeepLab – Panoptic Segmentation Module (PSM) |
Real-world Applications of Image Segmentation
Autonomous Vehicles
This industry requires vast amounts of data that need to be accurately annotated as it impacts human safety. Data annotation projects requiring different types of image segmentation are increasingly in demand as the race for level 6 autonomous driving intensifies. Here are some popular applications:
- Road Scene Understanding: Segmentation is utilized to understand the road scene by separating objects like lanes, vehicles, pedestrians, and traffic signs. This is important for self-driving cars to navigate safely.
- Obstacle Detection: By segmenting objects in the environment, autonomous vehicles can identify obstacles in the path and take appropriate action to avoid collisions.
Medical Imaging
Medical images often contain a lot of complex details. Types of image segmentation help isolate the specific areas that doctors care about, such as organs, tissues, or lesions. This allows for more focused analysis and diagnosis:
- Tumor Detection and Analysis: Types of image segmentation are crucial for identifying tumors in medical images such as MRI, CT scans, and X-rays. It helps in analyzing the size, shape, and location of tumors, aiding in diagnosis and treatment planning.
- Organ Segmentation: Segmentation is used to separate different organs or tissues in medical images for precise analysis, such as in brain segmentation for neuroimaging studies or heart segmentation for cardiac analysis.
Satellite Imaging
Aerial imaging captures images of the Earth’s surface from satellites orbiting the planet. These images provide valuable information about vast areas including the Earth’s land, oceans, atmosphere, and human activities. Thus, image segmentation enables the detection and analysis of these changes at a large scale with specific applications like:
- Land Cover Classification: Satellite images often require segmentation to classify different types of land cover such as forests, water bodies, urban areas, and agricultural land. This information is useful for environmental monitoring, urban planning, and agricultural management.
- Disaster Management: Types of image segmentation identify and assess the extent of natural disasters such as floods, wildfires, and earthquakes by analyzing satellite imagery. It enables efficient disaster response and recovery efforts.
Security and Surveillance
Types of image segmentation enable the detection and tracking of objects or individuals in surveillance footage, allowing security systems to monitor and analyze activities in real time. This helps us recognize potential threats or intrusions promptly. It provides the basis for various critical functionalities, including:
- Intrusion Detection: Types of image segmentation define suspicious activities or intruders in security camera footage by segmenting moving objects from the background.
- Crowd Analysis: Segmentation is utilized to analyze crowd behavior, count people, and detect anomalies in crowded scenes for enhanced surveillance and public safety.
Retail and E-Commerce
Within the retail sector, leveraging types of image segmentation proves invaluable for tasks such as product recognition, inventory management, and enhancing customer engagement.
- Product categorization: Employing segmentation techniques, products ranging from clothing to electronics and accessories on e-commerce platforms can be efficiently categorized. This facilitates personalized recommendations and targeted advertising.
- Object tracking and detection: By utilizing types of image segmentation, efficient shelf management is achieved, ensuring real-time tracking of individual items on store shelves and maintaining optimal inventory levels.
- Insight into customer behavior: Image segmentation gains valuable insights into customer flow patterns and product interactions, aiding in data-driven decisions for optimizing store layouts.
Industrial Inspection and Quality Control
Types of image segmentation are responsible for quality control, defect detection, and process optimization in manufacturing and industrial environments.
- Error localization: Image segmentation enables manufacturers to precisely identify individual defects on manufactured products and assess their impact on overall quality.
- Defect detection and classification: Types of image segmentation assist in the identification and classification of defects in various items, including automotive parts, electronic components, and consumer goods, streamlining the quality control process.
Image Segmentation: From various types to powerful impacts
The competitive tech landscape with complex IT issues promotes various technology ecosystems worldwide. Besides the three types of image segmentation mentioned above, we use a combination of techniques like Bounding box, polygon, and Lidar point cloud in projects from ADAS to retail and healthcare. The high-accuracy image segmentation technique, however, is a complex method that requires a skilled annotation team at an expensive budget. Choosing the right partner who is trustworthy and experienced in different areas is essential.
Read more: How to choose the best data annotation outsourcing company?
With over 7 years of experience and partnerships with industry giants like Qualcomm and LG, LTS GDS has tackled 15 million data points across diverse fields. From autonomous vehicles to manufacturing, our team empowers your projects. Ready to unleash the power of image segmentation? Contact us today!
