How image search works
 |
A common misunderstanding when it comes to image search is that the technology is based on detecting information in the image itself. But most image search works as other search engines. The metadata of the image is indexed and stored in a large database and when a search query is performed the image search engine looks up the index, and queries are matched with the stored information. The results are presented in order of relevancy. The usefulness of an image search engine depends on the relevance of the results it returns, and the ranking algorithms are one of the keys to becoming a big player. [2]
Modern image search engines increasingly utilize advanced technologies including Vision Transformers (ViTs), deep learning models, and multimodal AI systems that can interpret visual content directly. These systems employ computer vision and machine learning to understand and categorize image content beyond simple metadata, enabling features like visual similarity detection, object recognition, and reverse image search. [1] [3] [4]
Some search engines can automatically identify a limited range of visual content, e.g. faces, trees, sky, buildings, flowers, colours etc. This can be used alone, as in content-based image retrieval, or to augment metadata in an image search.
When performing a search the user receives a set of thumbnail images, sorted by relevancy. Each thumbnail is a link back to the original web site where that image is located. Using an advanced search option the user can typically adjust the search criteria to fit their own needs, choosing to search only images or animations, color or black and white, and setting preferences on image size.