Feature Extraction (SIFT, SURF, ORB)
In the previous lesson, we saw how Template Matching works. It was simple, fast, and useful — but it had serious limitations.
In this lesson, we learn the idea that changed Computer Vision completely: Feature Extraction.
This is where Computer Vision starts becoming robust, flexible, and intelligent.
Why Template Matching Is Not Enough
Template matching fails when:
- The object rotates
- The object scales up or down
- Lighting changes
- The background becomes complex
Real-world images are never perfect. So instead of matching the entire object, we focus on its important parts.
What Is a Feature?
A feature is a small, distinctive part of an image that:
- Is easy to recognize
- Stays stable under changes
- Looks different from surrounding areas
Examples of good features:
- Corners
- Edges intersections
- Texture patterns
Flat areas are usually not good features.
Key Idea Behind Feature-Based Matching
Instead of comparing entire images:
- Detect key points
- Describe what they look like
- Match those descriptions
This allows matching even when:
- Object is rotated
- Object is scaled
- Image is noisy
The Feature Extraction Pipeline
Almost all feature-based algorithms follow this pipeline:
- Keypoint Detection: find important points
- Feature Description: convert each keypoint into numbers
- Feature Matching: compare features between images
This pipeline is a foundation for modern vision systems.
What Is SIFT?
SIFT (Scale-Invariant Feature Transform) was one of the first powerful feature extraction algorithms.
Its key strengths:
- Scale invariant
- Rotation invariant
- Highly distinctive features
SIFT detects keypoints at different scales and describes them using local gradients.
Why SIFT Was Revolutionary
Before SIFT:
- Matching failed under scale change
- Rotation broke detection
After SIFT:
- Objects could be matched reliably
- Panorama stitching became possible
- Robust object recognition emerged
What Is SURF?
SURF (Speeded-Up Robust Features) was designed as a faster alternative to SIFT.
Main idea:
- Keep robustness
- Improve speed
SURF uses approximations to speed up computation, making it suitable for real-time systems.
SIFT vs SURF (Conceptual)
| Aspect | SIFT | SURF |
|---|---|---|
| Speed | Slower | Faster |
| Accuracy | Very high | High |
| Scale invariance | Yes | Yes |
| Rotation invariance | Yes | Yes |
What Is ORB?
ORB (Oriented FAST and Rotated BRIEF) was created to solve two problems:
- Speed
- License restrictions
ORB is:
- Very fast
- Free and open-source
- Suitable for real-time applications
Why ORB Is Widely Used Today
ORB trades a little accuracy for:
- High speed
- Low computation cost
- Easy integration with OpenCV
That makes ORB ideal for:
- Robotics
- Mobile vision apps
- Real-time tracking
Feature Matching (High-Level Idea)
Once features are extracted, they are compared using distance measures.
- Close distance → good match
- Far distance → bad match
Matching features allows us to:
- Align images
- Detect objects
- Track motion
Where You Will Practice This
Feature extraction is practiced using:
- Python + OpenCV
- Local machine or Google Colab
You will typically:
- Load two images
- Extract features
- Match them visually
When to Use Feature Extraction vs Deep Learning
Use feature extraction when:
- Dataset is small
- Real-time speed is critical
- Explainability matters
Use deep learning when:
- Large datasets exist
- Objects are complex
- End-to-end learning is needed
Practice Questions
Q1. Why are corners good features?
Q2. Which algorithm is fastest?
Q3. Which algorithm is most accurate?
Homework / Practical Thinking
- Take two photos of the same object
- Change angle or distance
- Think which features remain stable
This thinking prepares you for feature matching experiments.
Quick Recap
- Feature extraction solves template matching limits
- SIFT is accurate and robust
- SURF improves speed
- ORB is fast and free
- Features enable matching under transformations
Next, we move into Deep Learning for Computer Vision, starting with CNNs.