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Pictures, Not Words: All You Need to Know About Visual Search

Pictures, Not Words: All You Need to Know About Visual Search

Social media feeds are filled with pictures; news articles feature prominent images, and even our photo libraries clarify that we live in a visual world. 

Previously, we relied heavily on traditional text-based search engines to find information. But our demand for instant and visually appealing content has outgrown these basic tools. As a result, search engines have had to evolve. 

Google led the way by integrating advanced search features and later introduced Google Lens, which allowed users to search using images on their mobile devices. Microsoft soon adopted similar technologies, and now, the latest Samsung devices also boast these impressive capabilities called Circle to Search. This evolution highlights the shift towards more intuitive and visually driven search experiences.

Now, with a simple query, we can access a wealth of information on almost any topic imaginable within seconds. And while Google may be the most popular search engine, its success is not just due to brand recognition. Google's advanced technology and algorithms have been perfected over decades to match our search intent more closely than ever.

But the evolution of search engines doesn't stop there. Our visual world has brought about a new way of searching – through visuals.

What is the Definition of Visual Search?

Visual search is a technology where users search for information using images or other visual content instead of text.

Search engines, like Google or Pinterest, use computer vision technology to analyze an image's visual features and match it with similar images in their database.

For example, instead of typing "blue dress" into a search engine, you can upload a photo of a blue dress, and the search engine will show you visually similar dresses.

Reasons Behind the Recent Development of Visual Search

Several key developments have significantly shaped the recent trends in visual search:

Integration of Natural Language Processing (NLP) with image analysis

The combination of NLP and image analysis has been transformative. Systems now can interpret queries in natural language and convert them into visual searches, essentially turning text into images. This advancement allows for a more seamless and intuitive search experience.

Evolution of Deep Convolutional Neural Networks (CNNs)

The development of deep learning networks like ResNet and Inception has been groundbreaking. These networks are at the core of most image recognition and processing systems, greatly enhancing the ability to understand and classify visual content. Their ability to learn from vast amounts of data has improved the accuracy and reliability of visual search engines.

Rapid growth of Large Language Models (LLMs) and foundation models

The rapid advancement of LLMs and specifically foundation models has further propelled the capabilities of visual search technologies. These models provide a robust framework for processing and understanding complex visual data, contributing to more sophisticated and accurate search results.

These technological advancements have not only enhanced the functionality of visual search but also broadened its applications, making it an increasingly integral tool in various industries.

Popular Platforms Offering Visual Search Capabilities

Here are some examples of platforms providing Vvsual search features:

Google Lens

Google Lens is an image recognition software developed by Google in 2017. According to Google, people use Google Lens for 12 billion monthly searches.

Google Lens allows users to search for anything they see, including text, objects, and landmarks. With AI computer vision technology, Google Lens can identify objects, animals, plants, and landmarks. The app also allows users to point their phone's camera at text and translate it into different languages.

Pinterest Lens

Released in 2017, Pinterest Lens is a visual search tool that enables users to discover pins that are visually similar on the platform. However, unlike Google Lens, which works as a standalone app, Pinterest Lens is integrated into the Pinterest app.

Pinterest Lens has various use cases, including fashion and home decor inspiration. Users can take a photo of an outfit or room they like and find visually similar products on Pinterest to purchase.

Amazon Visual Search

Amazon's visual search feature is integrated into their mobile app. It allows users to take photos or upload images to find similar products on the platform. Like Pinterest Lens, this feature makes online shopping more convenient by allowing users to search for products instead of typing in keywords visually.

Bing Visual Search

Bing's visual search feature, released in 2014, allows users to perform reverse image searches. This means that instead of typing in text to find similar images, users can upload an image to find visually similar ones. Bing's visual search also has a "shop the look" feature where users can browse products in an image and purchase them directly from the retailer. 

In recent years, Bing has experienced significant advancements, notably since Microsoft's investment in OpenAI. This collaboration has propelled Bing's development, contributing to its enhanced functionality and user experience.

How Visual Search Works?

Visual search engines use advanced computer vision techniques like machine learning and neural networks to analyze an image's visual elements. The search engine's algorithm considers colors, shapes, textures, and patterns.

Algorithms work in two ways:

  1. Content-based image retrieval (CBIR): The search engine uses an image's visual elements to find similar images in its database. The features of an image are broken down into numerical values and then compared to the features of other images. The ones with the closest match are displayed as results.
  2. Search by image meta-data: In this method, the search engine uses metadata such as alt text, file name, and description to find visually similar images. E-commerce sites commonly use this technique to improve product recommendations.

Let's say you take a photo of a flower and upload it to your favorite search engine. The algorithm will analyze the image's colors, shapes, and other visual elements. Using reverse image search, an artificial intelligence-based algorithm will use these elements to find visually similar images and provide you with relevant results.

It's worth noting that this explanation provides a high-level overview of the classic flow of visual search. In contemporary contexts (as of 2024), advanced techniques like Large Language Models (LLMs) or foundation models, along with multimodal embeddings, are increasingly utilized. These approaches leverage vector databases to enhance the efficiency and accuracy of visual search processes. While not delving into intricate details, acknowledging these advancements contributes to a more comprehensive understanding of visual search technology.

What’s the Difference Between Visual Search and Image Search?

While image and visual search may seem similar, they are fundamentally different.

Image search is a traditional text-based method of searching for images. Users input keywords, and the search engine displays related images. Image search relies solely on text to generate results, whereas visual search analyzes visual elements in addition to text.

According to Neil Patel, 10.1% of Google's traffic is for image searches, which equates to roughly 1 billion daily users.

On the other hand, visual search allows users to upload or use an image from their device's camera. The algorithm then searches for visually similar images rather than relying solely on keywords. This method is especially helpful when users don't know the exact words to describe what they are looking for or if they want to explore visually similar options.

Another way to differentiate between these two methods is to use features search vs. conjunction search. In feature search, the user looks for a specific visual element, such as a red dress. In conjunction with the search, the user looks for multiple visual elements, such as a “red dress with polka dots”.

What are the Benefits of Visual Search?

Visual search has numerous benefits for both users and businesses.

More engaging user experience

The customer journey is now more visual than ever, and visual search makes it easier for users to find what they are looking for. Users can now interact with images instead of scrolling through endless text-based results, making the process more engaging and enjoyable. Visual search also increases time spent on a website or app, potentially leading to higher conversions and customer loyalty.

Faster and more accurate results

Typing in keywords and scrolling through pages of results takes time, and the results may not be what the user is looking for. Visual search allows users to find what they want with just one photo, saving them time and frustration.

Visual search uses advanced technology to analyze visual elements, making the search performance more accurate and efficient.

Personalized recommendations

Visual search provides personalized product recommendations based on a user's previous searches and preferences. By analyzing visual elements, the algorithm can suggest products that are visually similar to ones a user has previously shown interest in. Visual search takes the guesswork out of online shopping by providing relevant and customized options.

Bridging language and literacy gaps

Visual search is particularly helpful for users who may have difficulty with typing or spelling. As visual elements are universal, the language barrier is also reduced. Visual processing makes it easier for users to find what they want, regardless of their language or literacy level. 

Increased conversion rates

For businesses, visual information is more likely to catch the attention of potential customers than text-based information. Similar product recommendations based on visual elements or guide search results can lead to impulse purchases or increased customer satisfaction. Attention in visual search also means more time spent engaging with products, ultimately leading to higher conversions. 

Potential Risks of Visual Search

While visual search technology offers numerous benefits, it also poses several potential risks that need to be addressed:

Privacy issues

There are concerns about the use of images that may be copyrighted or used without permission, leading to potential intellectual property theft. For example, images can be scraped from the internet and used for machine learning purposes without the owner's consent, a practice reportedly common in some regions.

Inappropriate content

Visual search can inadvertently include sensitive or inappropriate content. For instance, an image of a child's bicycle might also contain the child, raising significant privacy and safety concerns. The technology lacks the human understanding needed to filter out such sensitive content.

Bias and inaccuracy

Visual search systems can suffer from biases and inaccuracies. Not all visual features can be accurately identified and matched, leading to incorrect or incomplete search results. This can frustrate users and limit the technology's effectiveness.

Vulnerability to attacks

Visual search systems can be susceptible to adversarial attacks. For example, altering a few pixels in an image can trick the system into misidentifying the content, such as mistaking a car for a hotdog. This lack of human-like understanding makes these systems vulnerable to manipulation by hackers.

Balancing the innovative potential of visual search technology with these challenges is crucial. By addressing these risks through robust privacy measures, improved content filtering, and enhanced accuracy, the full benefits of visual search can be realized while minimizing potential drawbacks.

Applications of Visual Search

Visual search applications are vast, and the technology is continuously evolving to be applicable in different industries.

E-commerce: Improving shopping experience

US internet users who regularly or always search for visual content before making a purchase account for 72% of the total. 36% of consumers have also used visual search when shopping online.

E-commerce giants like Amazon and eBay have already implemented visual targeting to improve customers' shopping experience. With visually similar recommendations, customers can quickly find what they want, leading to higher conversion rates and customer satisfaction.

Techno-savvy consumers increasingly turn to visual feature search, which saves them time and provides a more personalized shopping experience.

Fashion and apparel

Fashion industry professionals use visual search systems to analyze fashion trends and consumer preferences. This shift of visual attention also enables consumers to discover fashion inspiration and purchase items they like directly from the retailer, such as with ASOS’ Style Match, where they can upload an image directly from their computer to search for similar products.

A top-down processing example in everyday life is scrolling through a social media feed and seeing an outfit or accessory you like. With visual search, you can now easily find and purchase comparable items.

Visual search technology also aids in inventory management and product tracking for retailers. Using visual recognition technology, retailers can categorize and sort their inventory, making managing and selling products easier.

The growing e-commerce industry presents vast datasets ripe for analysis and research. For instance, the Fashion Product Images Dataset on Kaggle includes high-resolution product images, multiple label attributes describing the products, and descriptive text commenting on product characteristics. This dataset, which emerged from an open competition, offers a valuable resource for experimenting with and improving visual search technologies.

Travel and hospitality

Visual search technology is also used in the travel and hospitality industry, making it easier for travelers to plan their trips. Images of destinations, landmarks, hotels, and experiences can be used to find similar locations or experiences.

Guidance in the visual search process enables travelers to find hidden gems and tailor their travel plans according to their preferences.

The visual system is also used in hotel searches, enabling users to select rooms with specific views or amenities. Features like augmented reality enhance the booking experience, allowing customers to see a 360-degree view of their potential accommodation before booking. 


Learning in visual search is used in education for personalized learning and instruction. By analyzing visual elements, educators can identify students' strengths and weaknesses in different subjects.

Complex visual search tasks can also assess students' critical thinking and problem-solving skills. Visual search performance can also be used to embed tags and links in images to create interactive learning materials.

Visual search display also aids individuals with learning disabilities or those who struggle with traditional text-based methods. With visual aids, these individuals can have a more inclusive learning experience. The visual search paradigm is still developing, and its potential for educational purposes is immense.

Building your own Visual Search 

With visual search technology's increasing use and popularity, many businesses are curious about building their visual search engines. It is a worthwhile investment, especially for businesses in industries like e-commerce, fashion, and travel, where visual elements play a vital role in decision-making.

The technical process behind creating a visual search engine involves using image recognition and deep learning algorithms to analyze and categorize images. Artificial intelligence is used to train the algorithm on a large dataset of images. The algorithm then extracts visual features and creates embeddings, which are used for searching similar images.

Visual search software can integrate existing databases, websites, or eCommerce platforms by utilizing application programming interfaces (APIs) and software development kits (SDKs). 

Create a Visual Search Engine with iRonin.IT

At iRonin.IT, we can build visual search solutions for businesses of all sizes. Our team has image recognition, deep learning, and artificial intelligence knowledge. We can help you create a custom visual search engine for your business needs.

With 20+ years of experience in software development, we have the expertise and resources to develop a high-quality visual search system. Our team follows an agile development approach, ensuring continuous communication and efficient project management.

We can integrate visual search functionality into your existing system or create a standalone visual search engine.

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