AI In Visual Product Search_ Benefits And Use Cases

Visual search is a powerful tool that changes the way we engage with information, unlocking new possibilities and changing how we navigate our environment in a world that is becoming more linked and dynamic by the day. Its value goes beyond simple convenience.

Imagine yourself reading a menu in a different language or standing in front of a street sign in another country. With just a quick photo, you might have not just translations but also complex context. Additionally, think about using only the pictures that inspire you to start your search for the ideal house, which can take you to a wealth of listings and opportunities.

Here is the fantastic world of AI-powered visual search, where your camera’s lens opens up a universe of information and redefines how we interact, communicate, and take care of ourselves.

In this article, we look into the fascinating world of artificial intelligence (AI)-enabled visual search. We will also examine the relevance of technology, explain how it works, and consider how it has changed our lives.

Visual Product Search Market 

According to Zion Market Research, the global visual search market is expected to reach $28,470M by 2027.

Global Visual Search Market

Source: Zion Market Research

Moreover, in today’s image-driven world, captivating visuals are the key to unlocking explosive business growth. Discover the stats that prove it!

  • The human brain is wired for visuals: 90% of information transmitted is visual.
  • The market for image recognition is booming: it’s expected to reach $25.65 billion by 2019.
  • Consumers are demanding visual search: a majority of millennials prefer it over any other new technology.
  • Businesses are catching on: many advertisers see visual search as a top trend.
  • Retailers are adopting visual search: nearly half of retailers in the UK already use it.
  • Consumers are using visual search: over a third have already tried it.
  • Marketers are preparing for visual search: a sizeable portion plan to optimize for it by 2020.
  • Visuals drive product discovery: most consumers say visual search helps them develop their style and taste.
  • Early adopters win: brands that redesign for visual and voice search can increase revenue by 30% by 2021.
  • We love visuals: most people prefer visual information over text in most categories.
  • Visuals are king (especially for clothes and furniture): most respondents think visuals are more important than text overall, with a very high percentage prioritizing visuals for clothing and furniture.
  • Mobile users embrace visual search: a significant portion use it when available.
  • The market is growing: the global visual search market is expected to surpass $14.7 billion by 2023.

What Is Ai in Visual Product Search?

AI visual search is a technology through which users can get information using images rather than text-based inquiries. Users can start a search by utilizing images or photos captured with their devices (such as cellphones or cameras) instead of typing in keywords. 

After analyzing the visual aspects of the image, the system provides appropriate outcomes depending on the items or information seen in the picture and matches photographs with objects from a database by using sophisticated image recognition and machine learning algorithms. 

How Does AI in Visual Product Search Work?

A detailed relationship between image analysis, computer vision techniques, and machine learning powers AI visual search. This is an outline of how the procedure goes:

1. Image examination

Examining the uploaded image is the first step in the visual product search process. Computer vision algorithms analyze and extract information such as colors, textures, forms, and patterns from the picture. These elements are essential to comprehending the image’s visual content.

2. Extraction of features

Computer vision algorithms identify essential locations, objects, and colors in a picture using feature extraction. In order to help the system comprehend the image’s visual properties, this entails dissecting the image into quantifiable and comparable parts.

3. Catalog Comparison

A vast database or library of pictures, typically including product photos, item characteristics, labels, and descriptions, is compared to the extracted features. The search engine looks for things in the catalog that are comparable to the supplied image in terms of appearance.

4. Machine learning

Algorithms for machine learning support visual product search. The system becomes better at identifying patterns and connections between pictures as it analyzes more photos and gains knowledge from each encounter. Over time, the accuracy and precision of the system are improved through this adaptive learning process.

5. Search Suggestions

Using the comparison as a foundation, the system produces a collection of search results. The user often sees these results as product images with relevant metadata like labels, tags, or descriptions. After that, users can browse the visually matching merchandise.

6. User Feedback

The system learns from the way the user interacts with the search results. When a user clicks on an item or leaves comments, for instance, the algorithm gains more information about the user’s tastes and can modify its suggestions going forward.

7. Features

Visual product search is included in specific e-commerce systems. For example, the “camera search” feature of Amazon’s “Fire TV Stick with Alexa Voice Remote” allows users to take pictures of objects and find like ones.

To bridge the gap between visuals and information, visual product search makes use of machine learning, computer vision, and image recognition technologies. It changes the way consumers engage with digital platforms, making it easier and more natural for them to locate goods that match their visual tastes.

Different Stages In The Process Of AI-Powered Visual Search  

AI visual search process provides consumers with an engaging and natural method to interact with photos and find goods that are relevant to them. An outline of these phases and their uses is provided below:

1. Product Recognition

By uploading a photo of a particular object, such as a wedding dress, users may start a visual search. This intelligent program may do a variety of website and online sales channel searches to deliver precise information on the item’s availability across several retailers and pricing points.

2. Highly organized search

Users can locate and extract specific details from images. They can express interest in finding photos that resemble a particular feature, such as the carpet in a living room shot, by using cropping or zooming capabilities.

3. Accessory discovery

Visual search extends beyond standard product searches for accessory discovery. Users may ask for advice on recommended accessories, including what to wear with a garment or which pillows look good on a sofa.

4. Search for spatial references

This combination of picture recognition is beneficial for furniture apps like Amazon Showroom or IKEA, as it helps customers locate goods that meet specific measurements. Users may find things, for example, that go well with a specific patio or set of shelves.

Identifying the main product from a picture is very helpful to users. However, the promise of related material is what attracts merchants. This is recommending related products using search engines or internal visual search features on a website or app. This feature gives consumers more alternatives to choose from after their first search, significantly improving the buying experience. 

Implemented Technologies For Visual Product Search that uses AI

The following technologies are employed in AI visual search:

1. Computer Vision

By enabling computers to evaluate and understand visual features of images in a way that is similar to what humans see, computer vision plays a crucial role in visual product search. Product features, including form, color, texture, and pattern recognition, are made possible by it, making it possible to identify and match objects inside large catalogs accurately. By bridging the gap between customers’ visual inquiries and pertinent items, this technology makes shopping easy and simple.

2. Image recognition

Visual product search is improved by image recognition algorithms, which quickly recognize and classify items in photographs. Through learning from large-scale datasets, these algorithms identify unique features and properties that set different items apart. This feature expedites the shopping experience by effectively matching user searches with pertinent goods. It also simplifies product discovery based on visual qualities.

3. Deep learning

By breaking down photographs into useful attributes, deep learning improves visual product search. These characteristics allow for the accurate identification of product properties, including form, texture, and pattern, since they are extracted through many levels of analysis. By doing this, the system is able to match relevant products with user-generated visual searches, which expedites the process of finding desired items and improves the entire shopping experience.

4. Neural Networks

By decoding complicated pictures, neural networks—in particular, Convolutional Neural Networks (CNNs)—emulate human visual processing and provide visual product search. These networks, which are made up of linked layers, are able to accurately classify and understand images by identifying complex patterns, textures, and forms inside them. Neural networks employ this knowledge to identify features of items from photographs submitted by users, allowing for accurate product matching and identification across large catalogs.

5. Natural Language Processing (NLP)

When used with visual search, natural language processing (NLP) approaches improve the process of interpreting text associated with pictures by helping to provide accurate descriptions and contextual metadata. The system can produce more precise and informative product descriptions by training neural networks to recognize the connections between the text and the images. This will help users better understand and relate to the products that are displayed in search results, which will ultimately increase the effectiveness of visual product searches.

Use Cases of AI-Enabled Visual Product Search 

Instead of utilizing standard text-based questions, AI visual search enables users to search for information using images as queries. This technology evaluates and understands the information of images by utilizing computer vision and image recognition, opening up a variety of useful use cases in a variety of sectors. Here are a few examples of applications for visual search:

AI-powered visual product search is transforming the way people interact with products online and offline, offering a more intuitive and efficient experience. Here are some key use cases:

1. Effortless Product Discovery

With a picture or screenshot, users can find similar or identical products, bypassing the need for lengthy text descriptions. This is ideal for fashion, furniture, or home decor where visual aspects are crucial.

2. Improved Search Accuracy

AI can analyze product images and assign accurate tags, making text-based searches more effective. Even complex searches based on color, material, or style can yield relevant results.

3. Targeted Recommendations

AI can analyze a user’s search history and preferences based on past visual searches. This allows for displaying personalized product recommendations, leading to a more engaging shopping experience.

4. In-Store Product Exploration

Visual search can be integrated into retail apps, allowing users to scan product barcodes or take pictures of items in-store to access additional information, reviews, or similar products.

Advantages Of AI In Visual Product Search

Both consumers and sellers benefit from AI in visual product search.

I. For sellers

The advantages of visual search for sellers are numerous and improve both the general effectiveness of business operations and the shopping experience for customers. Below is an overview of these advantages:

AI-powered visual search offers a bunch of advantages for sellers, making it a valuable tool to boost customer experience and sales. Here are some of the key benefits:

  • Enhanced Shopping Experience:  Imagine a customer seeing a pair of sunglasses they love on a celebrity and being able to find similar styles on your website with a quick image search. Visual search makes this possible, creating a more engaging and intuitive way for customers to explore your products.
  • Reduced Customer Frustration: Text-based searches can be tricky, especially for products without specific names or with complex descriptions. Visual search eliminates this barrier. Customers simply upload an image and get relevant results, reducing the chances of them getting frustrated and abandoning their search.
  • Increased Sales Conversions: By making product discovery easier and faster, visual search can significantly improve your conversion rate.  People who find what they’re looking for quickly are more likely to buy.
  • Uncovering Customer Preferences: Visual search data provides valuable insights into what kind of products customers are interested in. By analyzing search queries based on images, you can identify trends and tailor your offerings or marketing strategies to match customer desires better.
  • Boosting Sales of Complementary Items:  AI can recognize similar items or even complementary accessories in the uploaded image.  This allows you to showcase relevant products alongside the search results, potentially increasing the customer’s basket size.
  • Combating Copyright Infringement: Some visual search solutions can be used to identify unauthorized use of your brand’s images online, helping you protect your intellectual property.

II. For Buyers

AI-powered visual product search can significantly enhance the shopping experience for buyers in several ways:

  • Effortless Searching:  Forget struggling to describe a product with keywords. With visual search, you can simply upload a picture of the item or use your phone’s camera to snap a photo. AI then works its magic, identifying the product and finding similar or exact matches from the retailer’s inventory.
  • Precise Results:  Text descriptions can be imprecise, but AI can analyze the image, recognizing details like style, color, brand (if visible), and material. This leads to more accurate results, saving you time and frustration from wading through irrelevant items.
  • Language Independence:  Language barriers are a thing of the past. Visual search eliminates the need for translation, making it a perfect tool for finding products while traveling abroad or shopping on international websites.
  • Enhanced Discovery:  Visual search can open doors to new possibilities. It can help you discover similar items in different styles, colors, or price ranges, inspiring you and potentially leading to a more satisfying purchase.

Customers can quickly find, examine, and interact with items using visual search in a way that appeals to their natural visual preferences. It makes buying easier, offers more alternatives, and guarantees a more interesting, effective, and personalized experience.

How Is AI Visual Search Different From Image Search?

Regular image search and AI visual search might sound similar, but they work in fundamentally different ways. Traditional image search relies on keywords you type in to find matching images.  For instance, searching for “red roses” would bring up pictures of red roses.

AI visual search, on the other hand, flips the script. Instead of text, you use an image to initiate the search. This image could be a product you saw but don’t know the name of, a plant you can’t identify, or even a screenshot of an outfit you like.  AI then analyzes the image content using machine learning to recognize objects, scenes, and even styles. 

With this understanding, it delivers search results based on what it finds in the image. So, if you snapped a picture of a flowering bush, AI visual search could identify the flower species or point you towards resources for plant care.

Here’s a table summarizing the key differences:

FeatureImage SearchAI Visual Search
InputTextual keywordsImages
TechnologyKeyword matchingImage recognition, machine learning
OutputImages based on keywordsInformation or similar images based on image content
ContextualLimited to provided keywordsUnderstands context and visual similarity
User ExperienceRelies on user’s ability to describeMore intuitive, users interact with images directly
ApplicationsE-commerce, stock photo websitesFashion, art, design, object recognition

Examples Of AI-Powered Visual Search Engines 

The following are some well-known AI visual search engines that have become more popular recently:

1. Google Lens

In 2017, Google introduced its AI-powered visual search tool. Originally a Google Pixel smartphone feature, it was eventually made accessible as an app for all Android handsets. Google’s primary search tools now include Google Lens. To improve its findings, it can distinguish items in the image and find comparable photos. To provide relevant results, it also uses language, phrases, and information from the websites that contain the photographs. Not only can Google Lens locate things, but it can also translate language, recognize animals, and research a variety of topics.

2. Pinterest Lens

Designed to serve users on the well-known social media site, Pinterest Lens first appeared in 2017. Through photos, it enables users to find similar things and novel concepts. While Pinterest Lens is limited to photos on Pinterest, Google Lens, and Bing Visual Search are available outside of their sites. With time, Pinterest Lens has added features like the Shop page that directs users to pins that may be purchased. It’s a recommended option for recipes, fashion inspiration, and home décor ideas.

3. Bing Visual Search

As an alternative to Google Lens, Bing Visual Search was first released by Microsoft in 2009 and relaunched in 2018. With the use of reverse image search and other visual search methods, it can locate picture sources, compare items, and recognize locations. It also helps artists and photographers find examples of their original work being uploaded and reproduced.

4. Snapchat Scan

Utilizing augmented reality and image recognition technologies, Snapchat Scan made its debut in 2019 and allowed users to perform native visual searches on the app. The scan was first developed to identify music, dog breeds, and plant species in addition to recommending camera lenses. Later, it was extended to provide fashion recommendations based on visual searches. With the help of increased capabilities, the technology can now offer music and filters depending on the content of images that are identified.

5. Amazon Stylesnap

In 2019, Amazon Fashion introduced StyleSnap, an image-based search engine. It was once focused on fashion, but as it grew, it added StyleSnap Home to serve clients looking for furniture. StyleSnap is a computer vision and deep learning application that helps consumers discover suggested products from uploaded photos.

These engines are unique in AI visual search because of their characteristics and applications, which meet different demands and interests. They are the perfect example of how artificial intelligence and image recognition are being used to improve user experiences and transform how we engage with digital material.


With technology always changing, AI visual search is a noteworthy development that has the potential to change the way we interact with information. Our online experiences are given a dynamic dimension by their capacity to understand and consider the visual environment. 

By allowing us to search and explore through images, visual product search creates new opportunities for engagement, problem-solving, and discovery. The way this technology develops and becomes more integrated into our daily lives, it will have a significant influence on a number of sectors, user interactions, and how we navigate our digital environment.

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Q. How can AI improve product search on the website?

A. Traditional text-based search can be limiting, especially for users who struggle to articulate their needs in words. AI-powered visual search allows customers to upload an image or take a picture of a product they like, and your website will surface similar or identical items from your catalog. This not only refines searches but also helps users discover complementary products they might not have found otherwise.

Q. What are some specific use cases for visual product search?

A. Imagine a customer seeing a pair of shoes on a celebrity and wanting the same ones. With visual search, they can snap a picture and find similar styles on your website.  Similarly, users can search for furniture or home decor items by uploading a picture of their desired aesthetic.   

Q. How does AI in visual search benefit businesses?

A. AI-powered visual search can significantly improve customer experience by making product discovery easier and faster. Additionally, by understanding what users are visually searching for, you gain valuable insights into customer preferences and buying habits, which can inform product development and marketing strategies.

Q.  Is AI-powered visual search expensive to implement?

A. The cost of implementing AI-powered visual search can vary depending on the complexity of your needs and the chosen vendor. However, compared to the potential increase in sales and customer satisfaction, the cost becomes an investment.  

Q. How do you get started with AI-powered visual search?

A. Several vendors offer AI-powered visual search solutions that can be integrated into your existing website or e-commerce platform.  Start by researching different providers and their offerings. Consider factors like pricing, ease of integration, and the level of customization available.  Many vendors offer free trials or demos, so you can experiment and see if the technology aligns with your needs.

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