image-search

Image search uses embeddings—numerical representations of visual content—stored in a vector database like Milvus for fast similarity matching. Load images, embed them with CLIP or equivalent models, index in the vector database, then query with a sample image or text description to retrieve visually similar results instantly.

Text-to-image search is supported through CLIP embeddings, which understand both images and text in the same vector space. Describe what you're looking for in natural language, embed that description, and query the vector database to find images matching your description.

Milvus is a scalable vector database designed for production image search workloads. It efficiently indexes and retrieves embeddings from large image catalogs, supporting fast similarity queries across millions of vectors in media archives and e-commerce applications.

CLIP handles standard image preprocessing internally, but you should prepare a clean dataset: consistent formats (JPEG, PNG), reasonable dimensions, and organized storage. Custom preprocessing scripts can normalize lighting, crop regions of interest, or resize images to optimize embedding quality for your specific use case.

Image-to-image search takes a query image as input and finds visually similar items—useful for product matching or finding duplicates. Text-to-image search accepts a written description and retrieves matching images—better for discovery when you don't have a reference photo.

Yes. Embedding-based similarity search is effective for detecting near-duplicate or policy-violating content. Index your flagged or reference images, then query new submissions to find visually similar items, enabling rapid moderation at scale across media archives.