Definition: Visual search is a search method that uses images — photographs, screenshots, or live camera input — as the search query rather than typed keywords. The search engine uses computer vision to analyse the image, identify objects, patterns, colours, and context, and return visually similar or semantically related results.
Why It Matters: Visual search is reshaping product discovery in e-commerce. Google Lens processes billions of visual queries monthly, and Pinterest Lens drives significant traffic for home, fashion, and food categories. For retailers, visual search reduces the “I can’t describe what I’m looking for” friction, increasing top-of-funnel discovery. Optimising product images — with clear backgrounds, multiple angles, and accurate alt text — improves visibility in visual search results.
How It Works: Visual search engines use convolutional neural networks (CNNs) trained on large image datasets to extract visual features (shape, colour, texture, object class) from an uploaded image. These features are converted into a numerical vector and matched against an indexed database of product or web images using similarity search. Tools like Google Lens, Pinterest Lens, and Bing Visual Search use this approach; e-commerce platforms can implement similar functionality via APIs from providers like Google Cloud Vision, AWS Rekognition, or Clarifai.
Related: AI-Powered Personalization, Machine Learning, Search Engine Optimization, Structured Data, AR and VR Marketing