How does visual search let shoppers find products from a photo?

Searching With an Image Instead of Words

Visual search lets a shopper upload or take a photo of an item — a jacket spotted on the street, a piece of furniture in a friend’s home — and receive a list of similar or matching products available for purchase, without needing to describe the item in words. This is especially useful for products that are hard to describe precisely, such as a specific pattern, an unusual shape, or a particular shade of color that doesn’t map neatly onto a standard search term.

The technology behind this shift is computer vision, a branch of AI focused on interpreting and analyzing visual information, applied specifically to matching images against a retailer’s product catalog.

Turning a Photo Into Something Searchable

When a shopper submits a photo, a computer vision model analyzes its visual features — shapes, colors, textures, and patterns — and converts these features into a numerical representation, often called an embedding, that captures the image’s key visual characteristics in a form the system can compare mathematically. The retailer’s product catalog has typically been processed the same way in advance, with each product image converted into its own numerical representation and stored for fast comparison.

Once the uploaded photo is converted, the system compares its representation against those of cataloged products, ranking results by how closely they match. This comparison isn’t a search for an exact pixel-for-pixel duplicate, but rather an assessment of overall visual similarity, which is why results are typically presented as a ranked list rather than a single definitive answer.

Where Visual Search Performs Best and Where It Struggles

Visual search tends to perform particularly well for visually distinctive product categories like clothing, footwear, and home decor, where color, pattern, and shape carry a lot of identifying information. It performs less reliably when a submitted photo is low quality, taken from an unusual angle, poorly lit, or cluttered with background objects that can confuse the model’s interpretation of what the actual item of interest is. Because visual search returns similar items rather than guaranteed exact matches, the specific product a shopper photographed may not appear if it’s not carried by that retailer or if it’s from a different brand entirely.

Retailers continue to refine these systems, often combining visual search with text-based filters, such as letting a shopper narrow visually similar results by price range or size, to improve the odds of finding a genuinely useful match.

Bottom Line

Visual search works by converting a submitted photo into a numerical representation of its visual features and comparing that against similarly processed representations of catalog products, ranking results by visual similarity. It performs best on visually distinctive items and can be affected by photo quality, though it doesn’t guarantee an exact match will exist in a given retailer’s catalog.

Go deeper

• How Does a Multimodal Model Process an Image Alongside Text?
• What’s the Difference Between No-Code and Low-Code AI Automation Tools?
• What Are Practical Use Cases for Multimodal AI?