Definition
Image classification is the task of assigning labels to an image as a whole, such as “cat,” “car,” or “tumor.” It is one of the core problems in computer vision.
Purpose
The purpose is to automate recognition of objects or categories in images for search, analysis, or decision-making.
Importance
- Foundational task in computer vision.
- Used in healthcare, retail, and safety monitoring.
- Limited in capturing multiple objects in a single image.
- Related to object detection and segmentation.
How It Works
- Collect and label a dataset of images.
- Extract features (traditionally) or use CNNs for representation.
- Train classifiers on labeled examples.
- Evaluate on unseen test data.
- Deploy to classify new images in real-world settings.
Examples (Real World)
- ImageNet Challenge: benchmark for image classification research.
- Google Photos: classifies images for search and organization.
- Medical AI: classifies X-rays into categories like “normal” or “disease.”
References / Further Reading
- Krizhevsky et al. “ImageNet Classification with Deep Convolutional Neural Networks.” NeurIPS 2012.
- Stanford CS231n Course on CNNs.
- IEEE Transactions on Pattern Analysis and Machine Intelligence.