Image search, reverse image search, visual search – What is the difference?

It can be difficult to distinguish between image search, reverse image search and visual search, and this is not made easier by the fact that they resemble in their name and also in their meaning. The key to their understanding is to think about them as parts (subfields) of each other. Among the three concepts, image search can be understood as a collective category. A search activity is considered to be an image search - irrespective of the type of data entered – if the results are returned as images. If you use an image (or an URL to an image) as input query, you will be doing reverse image search, or image-based search. Different search engines use diverse methods to implement image-based search. They often describe each image with metadata and keywords, and their result are images with similar properties. But nowadays, the best and most accurate solution is to rely on artificial intelligence and machine vision. These are combined by visual search, which uses pixel-to-pixel comparison to scan the images and uses the patterns found between the pixels as the basis for determining the results.

Our image-based searching solution supported with AI

We have developed our own image-based search solution, which is able to recognize the characteristics of a given image and then select pictures with similar features from a product database. The product catalogue of your online shop can also be used as the core database for our solution, so it can easily be optimized and deployed anywhere.

To illustrate our pioneering reverse image search engine in online shopping, the system was taught on a database of shoes. The following pictures show us how the system behaves in action.

In both cases, the first images were the ones we searched for and the other ones were given as results from the database used. We displayed the percentage of similarity indicated by the system above the images, which also served as the basis for the ranking of the results.

Simple text searches can also be supported with this technology, as the results can be refined by taking the visual information into account. For example, when searching for a mobile phone many search engines will start listing various accessories for that phone, this way improper product placements can greatly impair the user experience. On the other hand, if we analyse the content of the images in the results, we can already filter out the products that were not originally searched for.

Reverse image search in online stores to boost purchases

Reverse image search is a new business tool for online stores and its effectiveness and efficiency is just being recognized. Pinterest and Alibaba were the first to introduce an image-based search feature in 2014, but they didn't even think about such an extent change in the user experience and search habits of their visitors. Since then, many other companies such as Ebay and major fashion companies (H&M, Tommy Hilfiger, ASOS) have also developed their own visual search solution. The success of this technology is well-illustrated by the fact that Alibaba's image-based search engine is visited by over 10 million users on a daily basis. The popularity of this feature lies in the fact that users are eager to find the products they are looking for as quickly and easily as possible, so online businesses that utilize this technology can achieve tremendous business success.

Visual search can be used by online stores in two main areas. The first allows you to conduct a reverse image search, in which the picture contains a product, brand or pattern. The second generates relevant product recommendations using artificial intelligence to reveal similarities on viewed and/or favoured images. Both applications are designed to provide more and more appealing products for the customers.

Customers are often unable to put their ideas into words and this is especially true if they have to express themselves in a foreign language. Thus, image-based search can overcome huge language barriers, which would be difficult to solve without the technology.

Customers may also seek for clothes they found on their favourite celebrity or just saw on the street. Our AI development can be a great solution in such a situation: you can search for the given product in an online store with the help of just an image. Of course, there is a high chance that the same piece of clothing is not in the given store, so the program will list the best resembling products first, thus facilitating the purchase.

In general, much more emphasis is placed on visual factors in the case of offline shopping, but image-based search integrates those factors well into our online purchases. For example, it can be used to create sections that promote similar products. Here you can think of modules under a product that display relevant commodities, or even a feed of personalized products. User interactions play a major role when designing such surfaces, but we can provide a perfect foundation for such a system with visual search techniques.

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