Image embeddings are used to represent images as vectors in a high-dimensional space, capturing important features like color, texture, shape, and patterns in a compact form. These embeddings are generated using deep learning models, such as Convolutional Neural Networks (CNNs) or transformers. Once an image is transformed into an embedding, it can be easily compared with other images or used in downstream tasks.
One common application of image embeddings is in image search, where a user can submit an image as a query, and the system retrieves visually similar images based on their embeddings. Image embeddings are also used in tasks like image classification, where they help categorize images into predefined classes. In facial recognition, embeddings are generated for each face, and similar embeddings are used to identify individuals.
Image embeddings are highly effective because they allow for efficient similarity search and classification without needing to process the entire image. The embeddings can also be used in generative models like GANs to generate new images based on a given embedding vector, enabling the creation of new visual content or data augmentation in computer vision applications.