What is an Image Embedding?

Computer vision, a field focused on enabling computers to interpret visual inputs, utilizes image embeddings as a central concept for various tasks such as clustering, image comparison, and large multimodal models (LMMs). Image embeddings are numeric representations that encode the semantic content of an image, enabling comparisons with text embeddings for purposes like search and classification. A prominent model in this domain is CLIP, developed by OpenAI, which leverages over 400 million image-text pairs for zero-shot classification, allowing it to label images without fine-tuning. CLIP can perform tasks such as image and video classification, clustering, and semantic image search by comparing embeddings, facilitating applications like identifying content in videos or searching large image datasets efficiently. These embeddings, when stored in vector databases, empower semantic search engines to perform rapid and meaningful searches using natural language queries. The Roboflow platform is highlighted as a tool that employs semantic search powered by image embeddings to streamline finding images within datasets, illustrating the practical applications of embeddings in computer vision.