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Cover image for Image embeddings for visual search: how 'find similar' actually works
PRANJUL RATHOUR
PRANJUL RATHOUR

Posted on Originally published at pranjulrathour.scult.in

Image embeddings for visual search: how 'find similar' actually works

#vision #embeddings #search

'Find items that look like this one' is one of the most requested features in student e-commerce and catalog projects, and it works on the same principle as text retrieval: embed, store, search by similarity.

The pipeline

A vision embedding model turns each image into a fixed-length vector such that visually similar images land close together in that vector space. Store these vectors the same way you'd store text embeddings, and search with cosine similarity.

  • CLIP-family models embed both images and text into the same space, which also enables text-to-image search ('red running shoes').
  • Pure vision embedding models (not CLIP) can capture finer visual detail if your search is purely image-to-image.
  • Pre-process images consistently — same crop, same resolution — before embedding, or similarity scores get noisy.

See choosing between CLIP, ViT and multimodal LLMs for picking the right embedding model for this.

About Pranjul Rathour

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Pranjul Rathour

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Pranjul Rathour — GenAI engineer, Kanpur

Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-07.

Reach out if you want to talk GenAI, book a campus session, or invite him to judge:

Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: [email protected] · Invite me to your campus
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