Image ClassificationPixels, Features & CNN Architecture
Discover how computers convert visual images into numerical pixels, extract features using Convolutional Neural Networks (CNNs), and classify objects!
Overview
How computers identify visual objects
Image Classification is the process of an AI system taking an input image and predicting which category (class) it belongs to.
💡 Interactive Demonstration: Image ➔ AI Model ➔ Category
Click any image example below to see how the AI classifies it:
Predicted Outcome: Category: Cat (Mammal)
Confidence Score: 94% Confidence
Pixels as Features
While humans see shapes and colors, computers see images as numerical pixel grids. Each pixel is composed of 3 primary color channels: Red (R), Green (G), and Blue (B), with values ranging from 0 to 255.
Feature Math: An 8×8 image has 64 pixels. Multiply by 3 color channels (RGB) = 192 numerical features for the AI!
🎛️ Interactive RGB Pixel Mixer
Adjust the R, G, and B sliders below to see how pixel color feature values are calculated:
How CNN Works
Click stages to inspect the hierarchy
Image models use Convolutional Neural Networks (CNNs). They process images in a 4-step hierarchy:
1. Convolutional Layers
Scans image with filters to detect simple edges, lines, and textures.
Looking for: Horizontal & vertical lines, corners, color gradients.
Visual: 📐 Line & Edge Maps
2. Pooling Layers
Shrinks feature maps to reduce data size while preserving important shapes.
3. Fully Connected Layers
Combines detected shapes (wheels + doors) to make the final prediction.
Playground: Pixel Colour Classifier
Draw & Classify
Paint the 6×6 pixel grid using the color brushes, then click Classify Image to run the simulated model:
Flow: Training Examples ➔ Model Learns Patterns ➔ New Test Image ➔ AI Prediction
New Test Pattern: Image contains 80% Blue pixels and 20% Red pixels. What will the model predict?
Interactive Exercises
Exercise 1: Calculate Features
+15 XP · BeginnerExercise 2: CNN Layers
+20 XP · IntermediateExercise 3: CNN Hierarchy
+25 XP · IntermediateExercise 4: Need for Data
+30 XP · IntermediateKnowledge Check
Ready to test your knowledge?
Answer 10 multiple-choice questions to test your understanding of image classification and CNNs!
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