Visual embeddings
Fabric photographs become searchable feature vectors that support fine-grained visual comparison.
Applied Computer Vision
Swatch ID
AI-powered Fabric Identification
Identify upholstery fabrics in seconds using computer vision, visual embeddings, and local catalog retrieval.
The Problem
Fabric identification is still harder than it should be.
A physical swatch may arrive without a readable name or code. Lighting, texture, camera distance, color variation, and incomplete context can turn a simple catalog question into a slow manual search.
Swatch ID narrows the problem to something useful: match a normal photograph against a known fabric catalog and return the closest candidates. It is practical visual search, not generic image classification.
The Product
From photographed sample to a useful catalog match.
The lookup experience is intentionally direct: capture the fabric, search the indexed catalog, and review the leading result with confidence feedback. The interface keeps the model work in the background so the user can stay focused on identification.
How Swatch ID Works
A focused retrieval path from fabric image to ranked match.
The same visual representation used to index known catalog examples is used for each lookup image, making similarity search the bridge between a physical sample and its digital record.
- Known catalog
Reference fabric images and identifying metadata form the searchable collection.
- Preprocess
The photographed sample is prepared for consistent visual comparison.
- Generate embedding
A vision model converts the image into a numerical representation of its visual features.
- Search vectors
Similarity search compares the query embedding with indexed catalog examples.
- Rank candidates
The closest catalog results are ordered as likely matches rather than treated as generic labels.
- Return result
The interface presents the leading match with confidence-oriented feedback for review.
Behind the Lookup
Engineering for useful visual search.
Swatch ID combines computer vision and retrieval with an interface built around fast, confidence-aware lookup. The architecture stays grounded in a controlled catalog rather than claiming open-ended recognition.
Vector retrieval
Lookup is constrained to a known indexed catalog, keeping the result tied to useful fabric records.
Confidence feedback
The product surfaces match strength to support a practical human-in-the-loop decision.
Local-first design
Catalog images, embeddings, and retrieval can stay close to the workflow instead of requiring a public image service.
Real-World Value
Connecting physical fabric samples to searchable digital catalogs.
Swatch ID applies AI where the benefit is concrete: reducing lookup friction while keeping a person in control of the final identification. The same pattern can support material-heavy workflows where samples and digital records need a faster connection.
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Fabric catalog lookup
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Product-development sample identification
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Manufacturing support
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Internal reference tools
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Human-in-the-loop material identification
Computer VisionVisual SearchEmbeddingsVector SearchLocal RetrievalManufacturing AI
See the Workflow