Image classification

Image classification refers to labeling images with categorical labels. It is a core task in computer vision and visual recognition systems. Key aspects:

Images are analyzed to extract meaningful features and properties.
Features are used to classify the image into category labels.
Classical techniques like SIFT and HOG extract handcrafted features.
Machine learning models like CNNs now dominate for feature learning.
Public datasets like ImageNet and CIFAR-10 are used for training models.
Additional image data can enhance model robustness and generalization.
Applications include photo organization, robot vision, medical imaging, and more.

Image classification powers abilities like identifying people, objects, and scenes. High accuracies can now be achieved using deep convolutional neural networks trained on large labeled datasets.

Ongoing challenges include correctly classifying images with multiple objects, uncommon viewpoints, or ambiguity. And responsible data practices are imperative for classification.