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.
1. Who introduced the Support Vector Machine (SVM) model and in which year?
2. What is image classification primarily about?
3. What is the primary purpose of class scores estimation in image classification?
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.
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