AI in visual search: Use cases, technologies and benefits

Imagine standing before a foreign street sign or leafing through a menu in an unfamiliar language, and with a simple snapshot, gaining not just translations but also nuanced context. Further, consider setting out on a hunt for the perfect home with nothing more than the images that inspire you, leading you to a wealth of listings and possibilities. And then think about the transformative journey you can embark on towards better nutrition, where every meal becomes an opportunity to gain insights into its nutritional content simply by photographing your plate.

This is the remarkable realm of AI-enabled visual search, where the lens of your camera becomes a gateway to a world of knowledge, redefining the way we explore, communicate or nourish ourselves.

While AI-enabled visual search unquestionably proves its worth in numerous domains, its most profound impact can be witnessed in the realm of retail and e-commerce, where it enables users to simply point their smartphones or cameras at items they are interested in, bypassing the need for keywords or text-based queries.

AI-enabled visual search technology takes our understanding of shopping to a whole new level by seamlessly connecting the physical world with the digital realm. And as e-commerce evolves, this new way of searching might become the norm. A year after its official launch, Pinterest recorded an impressive monthly count of over 600 million visual searches conducted through its visual search engine, indicating the increasing importance of visual content discovery in the digital age.

In this article, we will dive into the fascinating realm of AI-enabled visual search, where we will demystify the technology’s operational mechanics, discuss its significance, and explore the transformative impact it has brought to our lives.

• What is visual product search?
• How does visual product search work?
• Stages in the visual search process
• Technologies used in visual product search
• Use cases of visual product search
  • E-commerce and retail
  • Art and culture
  • Travel and tourism
  • Real estate
  • Food and cooking
  • The automotive industry
  • Fashion and beauty
• Benefits of visual product search
• How is visual search different from image search? A comparative analysis
• Prominent examples of AI visual search engines

What is visual product search?

AI visual search is a technology that enables users to retrieve information by utilizing images instead of queries based on text. Instead of typing in keywords, users can input images or use pictures taken with their devices (such as smartphones or cameras) to initiate a search. The visual search system then analyzes the image’s visual characteristics and returns relevant results based on the content or objects depicted in the image. AI visual search technology relies on advanced image recognition and machine learning techniques to understand the content of the images and match them with relevant items, products, or information from a database. As per LinkedIn, the global visual search market exhibited a size of $6,669 million in 2019. Projections indicate a substantial expansion, with an anticipated value surpassing $28,470 million by 2027. This growth trajectory is underscored by a robust Compound Annual Growth Rate (CAGR) exceeding 17% from 2022 to 2030.

How does visual product search work?

AI visual search operates through a sophisticated interplay of image analysis, computer vision algorithms, and machine learning. Here’s a breakdown of how the process unfolds:

Image analysis: The process of visual product search starts with the analysis of the uploaded image. Computer vision algorithms dissect the image to identify and extract features encompassing colors, textures, shapes, and patterns. These features are crucial for understanding the visual content of the image.

Feature extraction: Computer vision algorithms pinpoint key points, objects, and colors within the image. This involves breaking down the image into components that can be quantified and compared, enabling the system to understand its visual characteristics.

Comparison to catalog: The extracted features are compared against an extensive database or catalog of images, which typically contains product images, item attributes, labels, and descriptions. The search engine endeavors to find matching items in the catalog that share similar visual attributes with the input image.

Machine learning: Visual product search is underpinned by machine learning algorithms. As the system processes more images and learns from each interaction, it refines its ability to recognize patterns and relationships between images. This iterative learning process enhances the system’s accuracy and precision over time.

Search results: The system generates a set of search results based on the comparison. These results are usually displayed to the user as product thumbnails accompanied by relevant metadata such as labels, tags, or descriptions. Users can then explore the visually matched products.

User feedback: The user’s interaction with the search results contributes to the system’s learning process. For example, if a user clicks on a particular item or provides feedback, the system learns more about the user’s preferences and can adjust its future recommendations accordingly.

Built-in features: Some e-commerce platforms integrate visual product search as a built-in feature. For instance, Amazon’s “Fire TV Stick with Alexa Voice Remote” employs a “camera search” function that enables users to capture images of items and discover similar products available on Amazon.

Visual product search harnesses the power of image recognition, computer vision, and machine learning to bridge the gap between images and information. It transforms how users interact with digital platforms, enabling them to find products that resonate with their visual preferences more intuitively and efficiently.

Stages in the visual search process

The AI visual search process involves several stages that offer users a dynamic and intuitive way to interact with images and discover relevant products. Here’s an overview of these stages and their applications:

Product identification: Users can initiate a visual search by submitting a photograph of a specific item they are interested in, such as a wedding dress. This advanced application can search various websites and online sales channels to provide accurate information about the item’s availability across different stores and at varying prices.

Detail-focused search: Users can identify and extract particular details within an image. Using cropping or zooming tools, they can indicate their interest in finding images similar to a specific aspect, like a carpet in a living room photo.

Accessory discovery: Visual search goes beyond direct product searches. Users can seek accessory recommendations, such as finding clothes that complement a jacket or identifying cushions that enhance a sofa’s appearance.

Spatial reference search: Particularly useful for furniture applications like IKEA or Amazon Showroom, this blend of image recognition assists users in finding products that fit within specific dimensions. For instance, users can discover items suitable for a particular terrace or a specific set of shelves.

For users, finding a primary product from a photograph is undoubtedly valuable. However, for retailers, the allure lies in the prospect of related content. This involves suggesting associated items through search engines or within a website or app’s internal visual search capabilities. This feature significantly enhances the shopping experience, providing users with additional options that complement their initial search. It’s an intelligent mechanism that not only aids users in making informed decisions but also cultivates an environment of exploration, where each discovery leads to another, creating a cycle of engagement that benefits both consumers and retailers.

Technologies used in visual product search

The technologies used in AI visual search are:

Computer vision

Computer vision plays a pivotal role in visual product search by equipping algorithms to analyze and comprehend visual aspects of images akin to human perception. It enables the recognition of product attributes, such as shape, color, texture, and patterns, allowing for accurate identification and matching of products within vast catalogs. This technology bridges the gap between users’ visual queries and relevant products, facilitating a seamless and intuitive shopping experience.

Image recognition

Image recognition algorithms enhance visual product search by swiftly identifying and categorizing objects present in images. By learning from extensive datasets, these algorithms discern distinctive attributes and characteristics that differentiate various products. This capability efficiently matches user queries with relevant items, streamlining the discovery of products based on visual attributes and expediting the shopping process.

Deep learning

Deep learning enhances visual product search by dissecting images into meaningful features. These features, extracted through intricate layers of analysis, enable precise identification of product attributes, such as shape, texture, and patterns. This process empowers the system to match user-generated visual queries with relevant products, streamlining the discovery of desired items and enhancing the overall shopping experience.

Neural networks

Neural networks, particularly Convolutional Neural Networks (CNNs), emulate human visual processing and bolster visual product search by disentangling complex images. These networks, composed of interconnected layers, learn to recognize intricate patterns, textures, and shapes within images, enabling accurate classification and understanding. By leveraging this learned understanding, neural networks enable the system to discern product attributes from user-uploaded images, facilitating precise identification and matching of products within extensive catalogs.

Natural Language Processing (NLP)

NLP techniques, when integrated with visual search, enrich the process of interpreting textual information linked to images, contributing to the creation of precise descriptions and contextual metadata. By training neural networks to understand the relationships between image content and accompanying text, the system can generate more accurate and informative product descriptions, enabling users to comprehend better and connect with the products displayed in search results, ultimately enhancing the effectiveness of visual product search.

Use cases of visual product search

AI visual search allows users to search for information using images as queries rather than traditional text-based queries. This technology leverages computer vision and image recognition to analyze and understand the content of images, enabling a range of practical use cases across various industries. Here are some examples of how visual search can be applied:

E-commerce and retail

Product discovery: Product discovery through visual search empowers shoppers with a seamless way to explore items they are drawn to. The system swiftly identifies identical items from an extensive e-commerce collection by simply capturing a photo of a desired product. This intuitive process streamlines the search experience, assisting users in effortlessly finding items aligned with their preferences.

Outfit matching: AI visual search enables users to upload images of desired outfits, prompting the system to suggest comparable clothing and accessories. This intuitive feature simplifies recreating preferred looks, making fashion exploration and coordination more accessible.

Home decor: Users can effortlessly capture images of furniture or decor items they wish to purchase. The system promptly identifies corresponding or identical items within online stores, presenting a convenient way to discover and acquire pieces that align with their interior design aspirations.

Art and culture

Artwork identification: Artwork identification becomes effortless with visual search. Users can take photos of paintings or sculptures, and the system promptly provides essential details such as the artist’s name, title, historical context, and additional information. This capability simplifies art enthusiasts’ exploration and deepens their understanding of artistic creations, fostering a richer appreciation for visual culture.

Museum Tours: Visual search enhances museum experiences and enables visitors to glean insights seamlessly. Users can swiftly access relevant information about the items they are interested in by capturing photos of artworks, artifacts, or exhibit plaques. This technology enriches museum tours, allowing attendees to delve deeper into the exhibits and gain a deeper understanding of each piece’s historical and artistic significance.

Travel and tourism

Landmark recognition: For travelers exploring new destinations, visual search offers a dynamic way to engage with landmarks, historical sites, and points of interest. Individuals swiftly obtain relevant details and historical context by capturing photos of these notable locations. This technology enriches the travel experience, enabling adventurers to connect more intimately with their surroundings and gain deeper insights into the cultural and historical significance of the places they visit.

Translation and localization: A visual search is a valuable language translation and localization tool. Users can photograph signs, menus, or documents in foreign languages, and the system promptly delivers accurate translations and contextually relevant information. This feature enhances communication for travelers, expedites comprehension in unfamiliar environments, and fosters cross-cultural understanding by bridging language barriers through the power of images.

Real estate

Property search: Visual search revolutionizes the process of home hunting for potential buyers. By capturing images of houses or neighborhoods they’re intrigued by, homebuyers can swiftly access listings of properties for sale. This technology streamlines the search for their dream home, enabling them to explore options that align with their preferences and visualize themselves in different spaces, ultimately simplifying the decision-making process.

Interior design: Visual search empowers users with a seamless way to furnish their spaces. Individuals can swiftly receive tailored suggestions for furniture and decor options by capturing images of rooms they seek to adorn. This technology enhances the design process, enabling users to visualize different arrangements and styles and facilitating informed decisions about how to enhance their living environments best.

Food and cooking

Nutritional analysis: Visual search technology offers a convenient method for individuals to monitor their nutritional intake. By taking pictures of their meals, users can leverage image recognition and analysis to estimate the nutritional content and calorie counts of the foods they consume. This process provides users with valuable insights into their dietary habits, helping them make informed decisions about their eating patterns and supporting their health and wellness goals.

Grocery shopping assistance: Visual search technology can aid users during shopping. Individuals can create visual shopping lists by taking photos of items they need or want to buy. The system can then identify these items and help users navigate the store efficiently, ensuring they remember essential ingredients and streamlining the shopping process.

The automotive industry

Car recognition: Visual search technology provides a streamlined approach for users considering purchasing a car. By capturing pictures of vehicles they are interested in, individuals can leverage image recognition to gather comprehensive information about the car’s make, model, features, and pricing. This capability empowers prospective buyers to make more informed decisions about their automotive choices, facilitating a more efficient and informed car shopping experience.

Parts identification: Visual search technology is a valuable tool for mechanics and car enthusiasts in automotive maintenance and repairs. By capturing images of car parts, individuals can leverage image recognition to identify specific components accurately. This capability streamlines identifying and sourcing replacement parts, assisting mechanics in their repair work and enabling car enthusiasts to understand their vehicles’ intricacies better. Visual search enhances efficiency and accuracy in automotive repair and customization, whether for routine maintenance or restoration projects.

Fashion and beauty

Cosmetic recommendations: In beauty and personal care, visual search technology offers a personalized approach for users seeking makeup and skincare guidance. By taking selfies, individuals can utilize image analysis to receive tailored product recommendations that align with their unique complexion, style, and preferences. This technology enhances the cosmetic shopping experience, providing users with a more accurate and informed selection of products that cater to their beauty needs and enhancing their self-care routines.

Hairstyle ideas: Visual search technology offers a novel approach for users seeking hairstyle inspiration. By uploading photos of themselves, individuals can harness the power of image recognition to receive tailored hairstyle suggestions. This process considers factors such as hair type, face shape, and personal preferences, providing users with valuable insights and recommendations to explore new looks that align with their unique features and style preferences.

These are just a few examples of the hundreds of potential use cases for visual search technology.

Benefits of visual product search

Visual product search yields advantages for both sellers and buyers.

For sellers

The benefits of visual search for sellers encompass a range of advantages that elevate the customer shopping experience and the overall effectiveness of business operations. Here’s a breakdown of these benefits:

Enhanced shopping experience: Visual search augments the shopping journey, improving user satisfaction and bolstering loyalty. Sellers create an engaging and memorable interaction that resonates with customers by offering a seamless and intuitive exploration process.

Reduced customer attrition: Visual search efficiency minimizes the likelihood of customers abandoning their search due to frustration. Quick and accurate results keep users engaged and committed to their shopping endeavors.

Increased sales: Precise search outcomes and complementary suggestions lead to higher average spending per user. The ability to provide tailored recommendations based on visual preferences fuels an uptick in sales.

Copyright vigilance: Reverse visual search equips sellers to identify unauthorized uses of their brand and catalog images. This capability aids in locating instances where images are being employed without proper authorization.

Targeted advertising: Visual search allows for more tailored and contextually relevant advertisements. For instance, if a user searches for a coat worn by a particular singer, ads can be strategically placed on platforms like Spotify when the user accesses the musician’s content.

Deeper customer insights: Visual search provides valuable insights into customer preferences beyond your product category. By understanding what inspires users and identifying related interests, sellers can refine their strategies and enhance each customer’s subsequent interactions on their website or app.

The integration of AI visual search opens doors to a realm of enriched customer engagement, streamlined conversions, and insightful analytics. This innovation enhances sales and customer satisfaction and amplifies a seller’s ability to tailor offerings and foster a lasting connection with their audience.

For buyers

Visual search brings a wealth of advantages to buyers, enhancing their shopping experience and making their journey through the digital marketplace more efficient and enjoyable. Here are how buyers benefit from visual search:

Discovering image sources: Visual search aids in identifying the sources of images, helping users explore new brands and stores they may not have encountered otherwise. For instance, recognizing a dress’s brand can lead to discovering a new favorite retailer.

Accessing higher-quality images: Visual search can lead to the discovery of higher-resolution images from low-quality photographs, offering more detailed views of products.

Seamless transition from physical to online shopping: AI visual search bridges the gap between seeing and purchasing an item. Users can instantly determine if a store is open, check availability, or explore similar options without encountering geographical or logistical obstacles.

Overcoming language barriers: Visual product search eliminates the need to translate desires into search terms. Users can find items without the challenge of describing intricate details like texture or color shades.

Time savings: Visual search eliminates the need to go through multiple pages and galleries when searching for specific reference items, making the process more efficient.

Inspiring new ideas: Advanced AI visual search allows users to compare options, budgets, and accessories, fostering creativity and exploration.

Stock availability solutions: If a desired product is out of stock, a visual search can identify other channels or suggest similar items, regardless of the brand.

Direct customer service interaction: Chatbots incorporating visual search assist users in finding products or accessories, streamlining the browsing experience without the need to navigate the entire catalog.

Visual search empowers buyers to swiftly discover, explore, and connect with products in a way that resonates with their natural visual inclinations. It simplifies the shopping journey, increases options, and ensures a more personalized, efficient, and engaging experience.

How is visual search different from image search? A comparative analysis

Here’s the comparison between image search and visual search:

Image search is primarily about finding similar or related images, while AI visual search extends to recognizing and identifying objects within images, making it more versatile for a range of applications.

Prominent examples of AI visual search engines

Here are some prominent AI visual search engines that have gained traction in recent years:

Google Lens: Google Lens, Google’s AI visual search technology, debuted in 2017. It started as an exclusive feature for Google Pixel smartphones but later became available as an app for all Android devices. Google Lens is now integrated into Google’s core search tools. It identifies similar images and can also recognize objects within the image to refine its results. Additionally, it employs language, words, and metadata from the images’ host websites to deliver relevant outcomes. Google Lens isn’t limited to finding products; it can also translate text, identify animals, and explore various subjects.

Bing Visual Search: Bing Visual Search, introduced by Microsoft in 2009 and re-launched in 2018, serves as an alternative to Google Lens. It can compare products, identify landmarks, and pinpoint image sources using reverse image search and other visual search techniques. Additionally, it assists photographers and artists in identifying instances of their original work being copied and uploaded.

Pinterest Lens: Pinterest Lens emerged in 2017, catering to users on the popular social media platform. It empowers users to discover analogous products and fresh ideas through images. While Google Lens and Bing Visual Search extend beyond their respective platforms, Pinterest Lens is restricted to images within Pinterest. Over time, Pinterest Lens has introduced features like the Shop tab, leading users to shoppable pins. It’s a preferred choice for finding home decor inspiration, fashion ideas, and recipes.

Snapchat Scan: Snapchat Scan entered the scene in 2019, leveraging augmented reality and image recognition technology to enable native visual search within the platform. Primarily used to recommend camera lenses and identify songs, dog breeds, and plant species, Scan later expanded its scope to offer fashion suggestions based on visual searches. The technology’s capabilities have been enhanced to provide filter and music recommendations based on the detected image content.

Amazon Stylesnap: Amazon’s visual search tool, StyleSnap, was launched in 2019 by Amazon Fashion. Initially geared toward fashion, it expanded to include StyleSnap Home, catering to customers seeking furnishings. Developed using deep learning and computer vision, StyleSnap assists shoppers in finding recommended items based on uploaded images.

In AI visual search, these engines stand out for their distinctive features and applications, catering to various interests and needs. They exemplify the strides in harnessing image recognition and AI to enrich user experiences and change how we interact with digital content.

Endnote

In the ever-evolving technological landscape, AI visual search is a notable advancement that can reshape how we interact with information. Its ability to comprehend and interpret the visual world offers a dynamic dimension to our online experiences. Visual product search opens new avenues for discovery, problem-solving, and engagement, enabling us to seek and explore through images. As this technology continues to mature and integrate into various aspects of our lives, its impact on industries, user interactions, and the way we navigate our digital surroundings is set to be profound.

Whether you are a beauty, fashion or real estate business, we help you integrate visual search capabilities into your applications. Partner with our artificial intelligence experts for robust visual search-based AI solutions that will allow your customers to search and discover products, information, and content using images instead of text.