Support vector machines — role?
Find optimal hyperplane with maximum margin.
Neural network — basic structure?
Layers of neurons with weights, biases, activation functions.
Local features — purpose?
Capture details from image regions.
Bag of visual words — process?
Clusters local features into codewords.
Neural network — training?
Adjust weights via backpropagation.
Activation functions — role?
Introduce non-linearity into neural networks.
Transfer learning — definition?
Using pre-trained models for new tasks.
Loss functions — purpose?
Quantify prediction error.
Class scores estimation — meaning?
Predict confidence for each class.
Answer 9 questions on Fundamentals of Image Classification and Neural Networks.
1. Who introduced the Support Vector Machine (SVM) model and in which year?
2. What is image classification primarily about?
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