Visual search is the update of text search: The search query is not a text, but consists of an image. The answer: What are the most similar products or data in my database?
Users can take a photo of a product or object and upload it via an app, bot, website or other tool in a visual search engine and get accurate information about it or recommendations based on similarity and style.
How visual search works in detail
The requested image is sent to a visual search engine. Like any text search engine, the visual search engine searches a specific data set (the search index) for similar products or data and returns them ordered by similarity.
If the visual search index consists of only two products, for example, a car and a dog, a search query with any car in the query screen will return the car as the most likely result, since there are no other similar options in the visual search index. search index.
- Visual search is not the same as image classification. The latter tries to find the general class like chair, table or car for an object in an image. Visual search, on the other hand, does not need a label to find an object correctly. classify, but must find the closest visually similar object in your data.
- Furthermore, visual search technology is not limited to finding visual similarities, but can be used with technologies such as OCR (Optical Character Recognition) to quickly generate information from an image and improve the results that the visual search engine offers.
The rise of visual search is driven by advances in computer vision, especially in the field of deep learning and neural networks.
Furthermore, thanks to the latest advances in in-memory database and indexing technologies, it is possible, within milliseconds, to search millions or even billions of objects or images.
Technical and organizational implications of a good search
The reason for the limited text-based search capabilities is not a lack of mature technology, but the challenge of providing and maintaining sufficient and consistent metadata for digital assets. The Manual Searching for keywords is still a reality.
Text-based search is also a tough nut to crack due to natural language ambiguity.
Organizations must define common vocabularies and ensure their use. External stakeholders and consumers however tend to stick to their own language. Voice search does not make searching easier because spoken language must also be converted to text. Visual search helps overcome these obstacles.
The image of a chair means the same thing in any language. Also, contrary to popular belief, in visual search you don’t need to tag images with specific tags or classes. This is a common but erroneous approach.
Modern high-precision visual search engines do not convert an image into single classes or tags that are human understandable. All the benefit of visual feedback would be lost if you tried to convert the visual search to a Force the text search pattern.
Visual search is still in its infancy, but it’s developing rapidly.
Visual search is technologically advanced and has great commercial potential. Global brands have quickly embraced visual search in their digital strategies. In some industries, the use cases were obvious from the start. Other Industries could also benefit from visual search, but still don’t have it at the top of their list of priorities.
When it comes to adoption of visual search, companies continue to split into innovators and early adopters. Since the technology is affordable and does not require large-scale implementation projects, the general introduction of the system is expected in the next few years.
Evaluation of a provider for visual search
Implementing visual search is an easy technology project if you work with a specialized visual search vendor to work together. A test can be done in one day without the need for integration. It’s a quick and convenient way to see if visual search technology is benefiting your customers.
Developing visual search in-house is also possible, but expensive. It requires a team of experts in deep learning, training and maintaining your models, and staying up to date with the latest scientific advances. In addition, backend engineers must create, maintain and to provide. The costs can easily run into the high 7-figure sum to put the first solution into production.
Here are six things to consider when choosing a visual search provider:
- Technological leadership: Visual search technology is rapidly evolving. Make sure your technology partner integrates new algorithms and constantly adapts your IT architecture. A high level of participation in R&D activities and regular updates are a good indicator. We strongly recommend trying different solutions before making a final decision. Make a decision. For a test, it is important to have the same and exactly the same test data. Use the requirements profiles.
- Methodology – Implementing visual search and developing it into a valuable and frequently used feature can be easy if your technology provider follows a comprehensive methodology. Find out in advance about the efforts required. Learn about the division of labor in ongoing collaboration.
- Data Protection – Today, users’ awareness of privacy is highly developed and expectations for data security are high. With its visual search application, it collects user-generated images, which its technology partner uses to improve reused applied machine learning algorithms. Make sure you have enough conviction Offer privacy options to your customers. Users should be able to easily select manual settings themselves. At least the following options should be available: Delete images immediately after extracting metadata and automatic deletion of user data such as geolocation, operating system and IP address.
- Pricing model – Due to technological advances, the cost of visual search has increased significantly in recent years. has been dropped. Most providers charge per request, with prices ranging from 25 cents to a fraction of a cent per request. it may be enough. Be sure to compare the total price of the app, also regarding the services and the total cost when you start your searches.
- Ensuring adoption – Visual search can be easily implemented, but something more is needed for it to become a generally accepted feature that truly offers added value. The functionality needs to fit well into the user interface, and companies need to incentivize users to try it out. It’s quite a learning curve to make Visual Search to be recognized as a useful extension. Businesses must advertise this additional search feature. If you are If you decide to add visual search to your customer experience, you should not only consider the technical aspects. but also ask yourself what your users can achieve with this feature, and plan your introductory communication accordingly. The use of visual search will continue to evolve.