Image Segmentation
So far, we have learned how to detect objects using bounding boxes. However, in many real-world applications, a simple rectangle is not enough.
Image segmentation goes one step further. Instead of drawing a box, it divides an image into meaningful regions based on visual similarity.
What Is Image Segmentation?
Image segmentation is the process of:
- Dividing an image into multiple regions
- Grouping pixels that belong together
- Separating objects from the background
Each segment represents an area that shares similar characteristics such as color, texture, or intensity.
Why Bounding Boxes Are Not Always Enough
Bounding boxes only tell us:
- Where an object roughly exists
They do not tell us:
- Exact object shape
- Which pixels belong to the object
Segmentation solves this limitation by working at the pixel level.
Real-World Applications of Segmentation
Image segmentation is used when precise understanding is required.
- Medical imaging (tumor detection)
- Autonomous driving (road vs vehicles)
- Satellite imagery (land classification)
- Photo editing (background removal)
How Segmentation Thinks Differently
Instead of asking:
“Is there an object here?”
Segmentation asks:
“Which pixels belong together?”
This makes segmentation more detailed but also more complex.
Main Types of Image Segmentation
Segmentation techniques can be broadly grouped into:
- Threshold-based segmentation
- Region-based segmentation
- Edge-based segmentation
- Clustering-based segmentation
Each approach follows a different intuition.
Threshold-Based Segmentation
Thresholding separates pixels based on intensity values.
Basic idea:
- Pixels above a threshold → foreground
- Pixels below a threshold → background
This method works best when:
- Foreground and background are clearly different
Region-Based Segmentation
Region-based methods group neighboring pixels that are similar.
The assumption is simple:
Pixels close to each other and similar in appearance belong to the same object.
This approach produces smoother segments.
Edge-Based Segmentation
Edge-based segmentation uses boundaries between regions.
Edges indicate:
- Sudden intensity changes
- Object boundaries
However, relying only on edges can be sensitive to noise.
Clustering-Based Segmentation
In clustering-based segmentation:
- Each pixel is treated as a data point
- Pixels are grouped based on similarity
Popular clustering methods include:
- K-means clustering
This approach is widely used in classic computer vision.
Segmentation vs Detection vs Classification
| Task | What It Does |
|---|---|
| Classification | Labels the entire image |
| Detection | Finds objects using boxes |
| Segmentation | Labels each pixel |
Segmentation provides the highest level of detail.
Challenges in Image Segmentation
- Lighting variations
- Overlapping objects
- Noise and shadows
- Complex backgrounds
This is why advanced models are often required for high accuracy.
Where You Will Practice Segmentation
You will practice segmentation using:
- OpenCV for classical techniques
- Python notebooks (local or Colab)
Deep learning segmentation will be covered later in this course.
Practice Questions
Q1. What is the main goal of image segmentation?
Q2. Which task labels every pixel?
Q3. When does thresholding work best?
Homework / Observation Task
- Look at photo background removal tools
- Notice pixel-level cutouts
- Compare them with bounding box outputs
This helps you visually understand segmentation power.
Quick Recap
- Segmentation works at pixel level
- More detailed than detection
- Used in medical, driving, and editing
- Foundation for advanced CV models
Next, we will study GrabCut Algorithm, a practical segmentation technique used in OpenCV.