Fundamentals of Image Classification and Neural Networks

📋 Course Outline

1. Image classification basics
2. Machine learning paradigms
3. Support vector machines
4. Neural network fundamentals
5. Bag of visual words
6. Neural network training
7. Forward propagation
8. Backpropagation algorithm
9. Activation functions
10. Image classification datasets
11. Transfer learning
12. Neural network loss functions

📖 1. Image classification basics

🔑 Key Concepts & Definitions

• Class of the object: The category or label assigned to an object within an image, such as "cat" or "dog," used for categorization in image classification tasks.

• Class label: A discrete identifier (e.g., "Car", "Tree") assigned to an object in an image, representing its category or class.

• Class scores estimation: The process of predicting numerical scores for each class, reflecting the likelihood or confidence that the object belongs to each class, often used to derive the final class label.

• Global features: Descriptive attributes extracted from the entire image, such as HOGs, LBPs, or Haar wavelets, capturing overall appearance or texture information for classification.

• Local features: Descriptors derived from specific regions or interest points in the image, such as SIFT + BoVW or SURF + BoVW, capturing local patterns and details relevant for distinguishing objects.