CS5720 - Week 10
Slide 186 of 200

Image Segmentation: Pixel-Level Classification

What is Image Segmentation?

Image Segmentation assigns a class label to every single pixel in an image, creating precise boundaries between different objects and regions.
Beyond Bounding Boxes:

• Pixel-level precision: Exact object boundaries
• Complete scene understanding: Every pixel classified
• Shape-aware: Handles irregular objects
• Dense prediction: Output same size as input
C
C
B
B
D
D
B
B
C
C
C
B
D
D
D
B
B
C
C
B
B
D
D
B
B
B
B
B
B
B
B
B
Key Insight:
Each pixel gets a class label: Cat (C), Dog (D), Background (B) - creating perfect object boundaries!

Segmentation Challenges

  • 🔍 Scale Variation
    Objects appear at different sizes requiring multi-scale processing
  • 🌫️ Boundary Ambiguity
    Fuzzy edges and unclear object boundaries are difficult to segment
  • 🎭 Occlusion Handling
    Objects partially hidden behind others need careful reasoning
  • 💾 Computational Cost
    Dense predictions require significant memory and computation
  • 🏷️ Annotation Difficulty
    Pixel-level labels are expensive and time-consuming to create
Memory Challenge:
A 512×512 image with 21 classes requires 5.5M output values vs 84 for object detection!

Segmentation Process Visualization

🖼️
Input Image
Original RGB image with multiple objects and complex backgrounds
🧠
CNN Processing
Deep network analyzes features at multiple scales to understand pixel context
🎯
Segmentation Map
Final pixel-wise classification with clean boundaries and accurate labels
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Prepared by Dr. Gorkem Kar