Image-based search works by analyzing visual content in an input image and comparing it to a database of images to find matches. Features such as colors, shapes, textures, and patterns are extracted using algorithms or deep learning models like CNNs.
These features are encoded into numerical vectors, which are then compared to vectors of other images in the database using similarity metrics like cosine similarity or Euclidean distance. Results are ranked based on how closely they match the input image.
Applications include visual product search in e-commerce, reverse image search, and identifying landmarks or objects from uploaded photos.