Worth a thousand words: why online stores need visual search

The statistics are brutal: 70% of users leave an online store if they haven’t found the product they need in a minute (Shopify). How can online retailers survive in such conditions and remain competitive? One possible solution is visual search. It makes the customer’s life easier and makes surfing the site much easier. Instead of text, there are now pictures, and stores have many new opportunities that will not only help them stay afloat, but also make good money.

Technology

Artificial intelligence has long been taking over the world. It has penetrated industry, medicine, education, computer games, and even creative fields such as marketing and journalism.

Ubiquitous neural networks have reached e-commerce. Thus, visual oman phone number data search is based on computer vision and deep

mobile phone number library

machine learning — a specially “trained” system of algorithms that can recognize products by images.

How was it taught? In a very exaggerated way, the process looks like this: the system is “fed” several million photos with images of a certain product. It analyzes its main attributes (color, size, material, silhouette, etc.) and remembers them.

Of course, this is not a matter of five minutes — it can take several months to train the system.

As a rule, neural networks undergo numerous tests to process data as closely as possible to the way the human brain would do it.

Search by color: similarity of the target-distractor; search by shape: set the size; search by color: set the size.
So far, such results have not been achieved, but everything is moving in that direction.

How it works

Despite the fact that the technology itself is very complex, using visual search is as easy as pie.

Instead of typing “red tight dress with a black zipper” in the input line, you just need to upload a photo/picture/screenshot of this dress to the website or application of the online store or paste a link to a post with it from Instagram.

The system first recognizes the image (reads its characteristic features and metadata), and then searches december 2021 november for similar images in its catalog, and, if necessary, directly on the Internet. And finally, it gives relevant possible products based on similarities – for example, color or style.

Ideally, visual search should find not only the desired india data product, but also related products (similar or related products) and form a complete set. For example, to offer that same red dress, also shoes and a handbag.

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