Welcome to the Image Classification section! Here, you can learn about various image classification techniques and their applications. Image classification is a fundamental task in computer vision, where the goal is to categorize images into predefined classes.
Common Image Classification Techniques
Convolutional Neural Networks (CNNs): CNNs are a class of deep neural networks that are particularly effective for image classification tasks. They automatically and adaptively learn spatial hierarchies of features from input images.
Support Vector Machines (SVMs): SVMs are a set of supervised learning methods used for classification, regression, and outliers detection. They work by finding a hyperplane in an N-dimensional space that distinctly classifies the data points.
Random Forests: Random Forests are an ensemble learning method that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Applications of Image Classification
Medical Imaging: Image classification is widely used in medical imaging to identify diseases such as cancer, fractures, and neurological disorders.
Agriculture: In agriculture, image classification is used to monitor crop health, identify pests, and estimate yield.
Security: Image classification is used in security systems for facial recognition, object detection, and tracking.
Further Reading
For more information on image classification, you can visit the following resources: