Image classification is a computer vision task that involves categorizing images into predefined classes or labels. It is a fundamental problem in the field of artificial intelligence and has numerous applications, including object recognition, face detection, and medical image analysis.
Several techniques are commonly used for image classification:
- Convolutional Neural Networks (CNNs): CNNs are deep learning models designed to automatically learn hierarchical features from images. They consist of convolutional layers that capture spatial hierarchies and local patterns, making them highly effective for image classification tasks.
- Support Vector Machines (SVMs): SVMs can be used for image classification, although they are more commonly applied in traditional machine learning. SVMs work by finding a hyperplane that best separates different classes in the feature space.
- Transfer Learning: Transfer learning involves leveraging pre-trained models on large datasets and fine-tuning them for specific image classification tasks. This approach is useful when labeled data for the target task is limited.
- Random Forests and Decision Trees: Ensemble learning methods like random forests and decision trees can be applied to image classification by extracting relevant features from images and making predictions based on these features.
To illustrate image classification, a common dataset is used from Kaggle consisting of handsigns. Each image in the dataset is labeled with the type of hand sign it is.
Explore the Dataset on Kaggle 🌐 View Dataset