Definition
Image segmentation is the process of dividing an image into multiple parts or regions that belong to the same class using criteria such as color or texture. This process is also referred to as pixel-level classification [1].
Basically, the goal of image segmentation is to assign each pixel of an image to categories like object, boundary, or region, to differentiate one part of the image from another. This detailed labeling helps in isolating objects, understanding the scene, and extracting pertinent information, establishing it as a fundamental step in numerous computer vision tasks.
Segmentation helps computers understand images better. Examples of such scenarios include separating the masquerade dancer from the village square crowd during New Yam Festival or identifying the bride wearing coral beads in a traditional Igbo wedding photo, cataloging traditional artifacts at a National Museum, analyzing medical imaging identifying malaria parasites in blood samples separate from healthy red blood cells, and automated quality control systems at Nnewi manufacturing plants distinguishing defective auto parts from good ones on assembly lines. It is like organizing goods at Ariaria Market, where each section represents distinct merchandise—shoes in one area, fabrics in another, electronics elsewhere.
An example of image segmentation in AI [2]
Origin
Image
segmentation concept originated in the 1960s and 1970s with on basic techniques
for separating objects from backgrounds. The early methods were based on edge
detection – identifying boundaries based on changes in intensity or color. The
1980s brought region-based methods that grouped pixels by shared
characteristics. Sophisticated clustering algorithms (k-means, water-shade)
emerged in the 1990s.
Machine learning arrival in the 2000s transformed segmentation into an automated, accurate process. Most recently, deep learning – particularly CNNS – has set new standards for segmentation accuracy. These advances now enable critical applications in medical imaging, autonomous vehicles, and object recognition [3].
Context and
Usage
The application
of image segmentation cuts across various fields. Some examples of how image
segmentation is used in different fields include the following:
- Robotics: Image segmentation powers robotics abilities for object recognition and manipulation. For instance, in industrial settings, robots utilize segmentation to identify and grasp specific objects, such as tools or parts.
- Art and design: Image segmentation is used in art and design for tasks such as image manipulation, color correction, and style transfer. Segmentation can help to separate objects or regions of an image and apply different effects or modifications to them.
- Medical imaging: Accurate segmentation is fundamental for treatment planning and overseeing disease progression. Also, image segmentation is usually deployed in medical imaging for tasks that include tumor detection, organ segmentation, and disease diagnosis.
- Surveillance: Real-time video analysis leverages image segmentation for detecting and tracking objects and people, assisting to identify and classify objects of interest, including suspicious behavior or potential threats.
- Agriculture: Image segmentation is deployed in agriculture for crop monitoring, disease identification, and yield forecasting, enabling farmers increase crop yields and make informed decisions about crop management.
- Autonomous vehicles: Accurate segmentation is essential for safe and dependable autonomous navigation. Autonomous vehicles utilize image segmentation to detect and classify objects such as other vehicles, pedestrians, and obstacles in their environment [4].
Why it Matters
Image
segmentation is crucial for image recognition system, extracting objects of
interest from an image for processing like recognition and description. Segmentation
accomplishes this through pixel-level classification.
Related AI
Applications and Use Cases
- Predictive Analytics: Using data and algorithms to forecast future outcomes and trends
In Practice
Meta's SAM is a good example of a real-life case study of image segmentation in practice. Meta developed the Segment Anything Model (SAM) to make image segmentation accessible to non-machine learning experts. Trained on over 1 billion tasks, the model makes accurate predictions on new datasets without additional training. SAM also handles complex semantic segmentation tasks including medical and satellite images [5].
References
- Klingler, N. (2023). Image Segmentation with Deep Learning (Guide).
- Cloudinary. (2025). AI Image Segmentation: How it Works (and Why it’s Important).
- Anglen, J. (2025). Mastering Image Segmentation: A Comprehensive Guide to Techniques and Applications.
- Buhl, N. (2024). Guide to Image Segmentation in Computer Vision: Best Practices.
- SuperAnnotate. (2023). Image segmentation detailed overview.