Image Classification is the process of assigning a label to an image based on its visual content. It is a supervised learning problem, where a system is trained on a set of labeled images and can then predict the label of an unseen image.
Image classification is a process of assigning a label to an image based on its visual content. It is a form of supervised learning, where a system is trained on a set of labeled images and then tested on a set of new images. The goal of image classification is to accurately identify the objects in an image, such as a person, animal, or object.
Image classification is used in a variety of applications, such as facial recognition, object detection, and medical imaging. In facial recognition, the system is trained to recognize a person’s face from a set of images. In object detection, the system is trained to identify objects in an image, such as cars, buildings, or animals. In medical imaging, the system is trained to identify abnormalities in medical images, such as tumors or lesions.
Image classification is a challenging task due to the complexity of the images and the variability of the objects in them. To address this challenge, researchers have developed a variety of techniques, such as convolutional neural networks, support vector machines, and random forests. These techniques are used to extract features from the images and then classify them.
Image classification is an important task in computer vision and has a wide range of applications. It is used in a variety of fields, such as facial recognition, object detection, and medical imaging. By using advanced techniques, such as convolutional neural networks, researchers are able to accurately classify images and improve the accuracy of their applications.