image embeddings search

If you mean searching images by embedding similarity, here’s the basic idea:

What it is

You convert each image into a vector embedding using a vision model.
Then, for a query image or text query, you compute its embedding and find the nearest vectors in your index.

Typical workflow

1. Extract embeddings
   • Use a model like CLIP, DINO, ResNet, ViT, etc.
2. Store embeddings
   • Save them in a vector database or ANN index:
     • FAISS
     • Milvus
     • Pinecone
     • Weaviate
     • Qdrant
3. Search
   • For a query image: embed it, then do nearest-neighbor search.
   • For text-to-image search: use a multimodal model like CLIP so text and images live in the same embedding space.
4. Rank results
   • By cosine similarity or dot product.

Common similarity metrics

• Cosine similarity: most common for normalized embeddings
• Dot product: often equivalent if embeddings are normalized
• L2 distance: also used, depends on the model/index

Example use cases

• Duplicate image detection
• Reverse image search
• Product search
• Similar photo recommendations
• Image classification via nearest neighbors

Minimal Python example with FAISS

import faiss
import numpy as np

# Example embeddings: 1000 images, 512-dim
xb = np.random.rand(1000, 512).astype('float32')
xq = np.random.rand(1, 512).astype('float32')

# Normalize for cosine similarity
faiss.normalize_L2(xb)
faiss.normalize_L2(xq)

index = faiss.IndexFlatIP(512)  # inner product = cosine if normalized
index.add(xb)

k = 5
D, I = index.search(xq, k)

print("Top matches:", I)
print("Scores:", D)

If you want text search over images

Use CLIP:

• embed text query with the text encoder
• embed images with the image encoder
• compare in the same vector space

If you want, I can help with:

• a full working example
• CLIP-based text-to-image search
• building a vector database pipeline
• or searching millions of images efficiently