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Commerce

Visual Search

Find products or images by uploading a photo. Vision matches style, color, and shape to the catalog. Fashion and home are where typing a query usually fails.

Interactive demo

Uploaded sneaker

Your photo

Visual matches

Scan the sneaker to find catalog matches.

Overview

How might we design visual search so people can trust and act on AI output?

When to use

  • Ideal for fashion retailers, home decor platforms, and visual product categories where image-based search is more intuitive than text descriptions.

When to skip

  • Text search already works well and photos add upload friction.
  • Catalog lacks visual embeddings or similar-item data.
  • Privacy-sensitive contexts where storing user photos is risky.

Rules

  • No crop or region select when the photo has multiple objects.

  • Results with no confidence or “similar, not exact” labeling.

  • Forcing camera access with no gallery upload fallback.

  • Returning unrelated items with no way to refine the match.

Evidence

ProductImplementation
Google LensPhoto search with object highlight and shoppable matches.
PinterestPin and lens flows that find visually similar ideas.
ASOSStyle Match from outfit photos to in-catalog items.
WayfairSearch by room photo for furniture and decor matches.

FAQ

What is visual search in e-commerce UX?

It is search by image: users upload or capture a photo and the system finds similar products in the catalog without requiring a text description.

When does visual search work best?

Fashion, furniture, and other visual categories where describing an item in words is hard but a photo is easy.

What should the results UI show?

Similarity ranking, which part of the image matched, and filters to narrow color, price, or category.

How do you handle bad matches?

Let users crop, pick a detected object, or fall back to keyword search. Silent bad results erode trust fast.