How Visual Search Technology (Vision AI) is the future of online search experience

Human and animal brains recognize objects easily, but computers struggle with this task. The latest technology is being advanced and we are experiencing these innovative technologies making things easier in our lives.

Google search is the best example of such technology that has become a generic part of our life. Every one of us is using google search to find information for many little and simple things.

How Computer Vision Works

Whether we got a cough or we want a recipe to make food, to go somewhere, or just random thought that came to our mind; we just Google it to make sure to be up-to-date with our knowledge.

Besides this, everyone appreciates the comfort of online shopping. Just a few clicks and you can get any product you like.

In the context of the latest technology, a new term is taking its roots strongly, i.e., "Computer Vision or Image Recognition"

Visual search is the ability of software to identify objects, places, people, writings, and actions in images. Vision can use AI techniques in combination with computer/mobile app cameras and artificial intelligence software to achieve image recognition.

For the eCommerce and education sectors, there is huge growth potential. That's why Pinterest, Google, and Amazon are the main visual search engines today. Microsoft has also developed impressive computer vision capabilities for its Bing search engine.

How to build Visual Search Application

In this article, we are going to look at a specific subset of AI called Vision AI or Computer Vision.

A host of retailers including ASOS, Wayfair, Neiman Marcus, Argos, and IKEA have all built proprietary visual search tools.

We will discuss how new technology is taking its roots in online search methods through "Vision AI".

Topics I covered in this article -

• What is computer vision?
• How vision AI or visual search is redefining the future of Online search
• The History and Evolution of Computer Vision
• Impact of Vision AI: Why is visual search important?
• Applications of Image Recognition/Visual Search
• Advantages of Visual Search Technology
• Other Visual Search Engines and APIs
• Visual Search Statistics

Let's begin.

What is computer vision?

Computer vision or visual search refers to a subset of AI that collects information and multi-dimensional data and uses it to process, analyze, and utilize visual data in the same way as humans do.

The collected data depends on the processed images and the data at the pixel level which helps in decision making.

Vision AI - Introduction

Visual search is an emerging development in the world of AI and ML, which has the potential to revolutionize the way consumers find and buy products.

Image recognition is used to perform many machine-based visual tasks, such as labeling the content of images, searching for image content, in autonomous robots, self-driving cars, accident prevention systems, etc.

Searching by Images/photos is no longer a difficult task, as an image reverse search computer vision is readily available to assist you round the clock.

This "search by image feature" uses your phone's camera to identify objects and then gives you contextual information about those objects; real-time reverse image lookup.

How to use Google Image Search to find information about a picture

These tools allow you to conduct a reverse search image via drag and drop.

62% of Millennials want the ability to search visually on any other new technology, and people like Google, Amazon, Pinterest, and Bing have already developed significant capabilities in this area.

How vision AI or visual search is redefining the future of Online search?

Image recognition is the newest technology that helps us to get search results just by clicking an image. With vision AI, we can click or upload an image to our portal (Google lens, Amazon) and get relevant results without frictional interaction on mobile.

For example, if you want to search for a dress that you don't know from which brand or website it is, you can't search it by keywords. For this situation, you can simply upload the image of that dress, and Boom! all related details and relevant results you can get with this visual search technology.

But this technology is not just limited to the e-com sector. It can be useful in educational and other sectors to get the fast and the best information about an item. Below I metioned popular educational app loaded with image search technology.

1. Amazon Flow Uses Instant Image Recognition To Search Store-

2. Pinterest Mobile App Image Search -

3. How Google Lens helps you search what you see | Search

4. Calorie Mama

Automatically count calories by simply taking a food photo!

5. PictureThis - Plant Identifier App

PictureThis - Instantly identify plants It's so cool, with PictureThis app you can identify plants, flowers, and trees instantly by just taking a snap, let you become a plant expert."With this app, if you click a picture of a Mango then vision AI could provide you search results about the Mango and its benefits and more recipes to educate you".

6. How To Identify Plants & Flowers With Your iPhone Camera! No App Needed!

Here is how you can easily identify plants, flowers, trees, bushes, fungi, and more just using the Camera and Photos app of your iPhone with iOS 15!

7. Bing Visual Search

Now you can search, shop, and learn more about your world through the photos you take. These features currently available in the U.S.

8. Camfind

Have you ever wanted to Search the Physical World™? Enter CamFind. The world’s most accurate mobile visual search engine, powered by the CloudSight Image Recognition API.

Other Examples of Visual Search

1. Vision AI in e-comm

2. Vision AI in Airport

3. Visual search to find the nearest restaurant

4. Visual search by food photo

The History and Evolution of Computer Vision

The first experiments for computer vision were in the 1950s using neural networks to detect the edges of an object and sort simple objects such as squares and circles.

Later in the 1970s, the commercial practice of computer vision was implemented. It was the interpretation of handwritten text using optical character recognition (OCR). This execution was used to interpret written text for the visually impaired.

In the 1999s, as the Internet gets advanced, facial recognition programs flourished. Later, in 2010 (and beyond), deep learning helped computers train themselves and self-improve over time.

Today, this technology has found its use cases in various domains ranging from Automotive, Healthcare, Retail, Smartphones, etc.

The AI in computer vision market is estimated to be valued at USD 15.9 billion in 2021 and reach USD 51.3 billion by 2026, at a CAGR of 26.3%.

Vision AI is a platform that allows you to create, deploy and track any kind of vision app in seconds, and allows you to create your custom vision apps.

Image Recognition technology uses machine learning to identify objects and people in images, videos, and other input. It can recognize things like faces, landmarks, and logos - so you can use it for traffic or building signs.

Visual search allows people to find what they are looking for without the need for words to describe it. It allows you to better capture insights from your images, and videos & execute that data to improve the user experience.

Today, computer vision has experienced a real boom. It is the backbone of an autonomous future in many industry sectors including transportation, healthcare, agriculture, retail, manufacturing, etc.

• In May 2021, Tesla announced that it would be switching entirely to Tesla Vision, a camera-based Autopilot system, retired radar.
• In retail, it can be used to provide insights into consumer behavior that retailers can apply to create better customer experiences.

When it comes to adopting and implementing computer vision, the first step is to recognize how it can serve people and make their lives easier - in behavior and form.

• If we are in a foreign country where we do not speak the language or cannot read/understand the menu to order lunch or while driving on the highway, we can't understand the street signs, visual search can help us to deal with all these problems in our regular life.
• By applying techniques such as 3-D reconstruction, current generation hardware can reach the required degree of accuracy and speed at the edge.

Today, no matter who we are, computer vision is already making our lives better.

• Face ID technology began by enabling people to unlock their phones and has now been adopted by mobile applications for services such as investment and banking accounts that require a high level of security.
• Computer vision will already be essential to the development of the Fourth Industrial Revolution, as it continues to automate traditional manufacturing and industrial practices.

It is an open-source technology that can be used to create applications for your business, analyze the data, and build your systems easily.

Image recognition's broad functionality can enable many transformative user experiences, including but not limited to:

• Automatic image organization
• User-Generated Content Moderation
• Advanced visual search
• Automatic photo and video tagging
• Interactive Marketing/Creative Campaign

It includes some of the primary ways where image recognition is shaping our future.

With wide implications in our routine lives, UX designers can create wide accessibility by using visual API. Visual search has many applications as below:

1. Image Segmentation

It is the process of dividing an image into multiple regions and fragments based on the pixel characteristics in an image. Commonly used for investigative purposes, image segmentation involves separating the foreground from the background based on similarities in color or shape, or clustering parts of an image by pixel. Parts of the image are differentiated by colors.

2. Object Detection

In this field of computer vision, AI is concerned with detecting one or more objects in an image or video. For example, surveillance cameras cleverly detect humans and their movements (no movement, objects like guns or knives, etc.) to take precautions for suspicious activities.

3. Facial Recognition

The purpose of facial recognition technology is to detect an object or a human face in an image. It is one of the complex applications of computer vision due to the variability in human faces- expression, posture, skin color, differences in camera quality, position or orientation, image resolution, etc. However, this technique is majorly used. Smartphones use it for user authentication. Facebook uses the same technique when it offers tagging suggestions for people in a photo.

4. Edge Detection

Edge detection can be helpful in data extraction and image segmentation. Edge detection is finding the edges of the boundaries of objects within an image. This practice is done by detecting the imbalance in brightness.

5. Pattern Recognition

A pattern can be a recurring sequence of data or a set of data added to the system, and pattern recognition is the ability to detect the arrangement of data or characteristics of a system.

6. Image Classification

Image classification involves classifying an image based on the relevant visual content present in it. This process involves focusing on the relation of adjacent pixels. The classification system consists of a database containing predefined patterns. These patterns are compared with the object discovered to classify what it is. Image classification has important applications in areas such as vehicle navigation, biometry, video surveillance, biomedical imaging, etc.

Advantages of Visual Search Technology

The Vision AI is powered by artificial intelligence that learns as you interact with it, it is an intelligent, data-driven approach to understanding your customers to help you sell more.

• Detect objects and faces, read handwriting, and create valuable image metadata with the Vision API.
• Use machine learning to interpret your images with industry-leading predictive accuracy.
• AutoML Vision can help to train machine learning models that classify images by your custom labels using.
• One of the uses for image-to-text conversion is to use the generated text on your webpage. This can help create more relevant searches and allows people to search for images and retrieve text.
• Prerequisites for converting an image to text; you can convert various image formats such as PNG, JPEG, GIF, or PDF.
• Automatically detects and converts the language used in a document.
• Sometimes buyers like a style, but they don't know how to describe it in a keyword search. Visual search technology provides customers with exactly what they're looking for, by image and helping them order matching products on the spot.
• This technology makes it easier for customers to find the products they want by searching by image or photo instead of a keyword.
• Helps to connect customers with similar products through visual search and computer vision to increase their buying potential and cross-selling.
• With the help of computer vision and AI, shoppers could find compatible products they aren't even looking for.
• Helps people complete the look of a room, fashion outfits, and more, to serve their unique preferences throughout the buyer journey without knowing the product details.
• Suggestions for similar or equally relevant product suggestions to reduce bounce rates, increase basket size and increase sales.
• With visual product search, retailers can create an engaging mobile experience that enables customers to upload a photo of an item and instantly view a list of similar items for purchase.
• You can easily scan barcodes, detect faces and objects, read handwriting, and create valuable image metadata. Vision API gives developers access to the world's most advanced computer vision technology.
• With Vision AI, you get a bold and beautiful artist in the cloud who can read what's on any device, translate even the toughest handwriting to text, or accompany you directly via voice recognition can negotiate.

Technology has been developed to provide useful services to businesses and individuals. Users can detect objects and faces, read handwriting, and create image metadata using image data captured by a mobile phone's camera.

Visual Search Engines and APIs

The top tech giants are already using visual search technology to compete with new trends:

1. Google Vision AI

Google Vision API provides powerful pre-trained ML models through REST and RPC APIs. it assigns labels to images and quickly then categorizes them into millions of predefined categories. Can detect objects and faces, and read printed and handwritten text, to build

2. Microsoft Bing Visual Search API

Microsoft Computer Vision API implements successful computer vision with ease. It adds leading-edge video and photo recognition technology to your apps with a simple API call.

3. OpenCV (Open Source Computer Vision)

OpenCV is a tool for performing image processing and computer vision tasks. OpenCV provides a real-time optimized Computer Vision library, tools, and hardware to perform tasks like face detection, landmark detection, object tracking, etc. It supports many computer languages including Python, Java, and C++. It also supports model execution for Machine Learning (ML).

4. Amazon Rekognition

Amazon Rekognition is a cloud-based computer vision platform that was launched in 2016. You can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content with this.

5. IBM Watson Visual Recognition

IBM Watson Visual Recognition is a service on the IBM Cloud that enables you to create smart applications that perform content analysis on visual Content. It can tag, classify, categorize and discover visual content using machine learning.

Visual Search Engines and APIs -

• Snap Tech Visual Search for Oracle Commerce Cloud.
• Einstein Vision.
  
• Clarifai.
• Amazon Web Services DeepLens
• Amazon Web Services visual search code.
• Syte
• Klevu
• YesPlz AI
• Visii
• nyris
• Pixolution
• Same.energy

Visual Search Statistics

• 90% of information transmitted to the human brain is visual.
• The human brain can identify images seen for as little as 13 milliseconds.
• The image recognition market will grow to $25.65 billion by 2019.
• 62% of millennials want visual search over any other new technology.
• 21% of advertisers believe visual search is the most important trend for their business in 2019.
• 45% of retailers in the UK now use visual search.
• 36% of consumers have conducted a visual search.
• 55% of consumers say Visual Search is instrumental in developing their style and taste.
• Visual information is preferred over text by at least 50% of respondents in all categories except for electronics, household goods, and wine and spirits.
• 59% think visual information is more important than textual information across categories (vs. 41% who think textual information is more important).
• When shopping online for clothing or furniture, more than 85% of respondents respectively put more importance on visual information than text information.
• 20% of app users make use of visual search when the feature is available.
• The Global Visual Search Market is estimated to surpass $14,727m by 2023 growing at a CAGR of +9% during the forecast period 2018–2023.

Pinterest Visual Search Statistics

• Brands can now target over 5,000 categories through visual search advertising on Pinterest Lens.
• Image-based Pinterest Ads have an 8.5% conversion rate. Heap Analytics
• 21% of Pinterest users use traditional search less when they have an option to use visual search.
• There are over 600 million visual searches on Pinterest every month.
• 80% of Pinners (those who use the Lens technology every day) start with a visual search when shopping vs. 58% of non-Pinners.
• When shopping online for clothing or furniture, over 85% of respondents put more importance on visual information than text information.
• 49% of Pinners say they develop a better relationship with brands they love through visual search.
• 61% of consumers say visual search elevates their experience while in-store browsing.

Visual search statistics infographic

• ASOS Style Match allows shoppers to purchase using images.
• eBay debuts visual search technology.
• Blippar uses visual search to help customers find their way.
• EasyJet launches a visual search tool.
• Levi’s and Disney experiment with visual search on Snapchat.
• Walmart is testing an in-house visual search technology.
• Salesforce launches Einstein visual search for retailers.
• CarStory launches the first automotive visual search app: Business Wire.
• Snapchat’s Lens Creative Partners program helps brands find AR filter creators.
• Farfetch launches a visual search app, allowing users to search for images they found on Instagram or Pinterest.
• Marks and Spencer launch new mobile visual search technology.
• AI Startup Site Helps Retailers Enable Visual Search Capabilities.
• Lifestyle adds new retail technologies, including voice and visual search.
• ViSenze brings visual search to Samsung smartphones.
• Slyce debuts a visual search for grocery stores.
• Argos launches AI-powered visual search to connect offline stores with an online catalog.
• Lush demos visual search app and fresh ‘digital packaging’ at SXSW.
• LexSet debuts new visual search software.
• Argos launches a visual search service for app users.
• IKEA revamps the app to allow a shoppable visual search at home.
• Cadeera Visual Search Technology Raises Seed Money for AI-Based Home Decor Service

Visual Search Marketing: Brands Using Visual Search Today

• Forever 21 increases AOV by 20% through visual search.
• BooHoo enjoyed an 85 percent higher conversion rate for those using Camera Search, as compared to those who did not.
• Wayfair uses visual search to improve the customer experience.
• Walmart Turns Its iPhone App’s Barcode Scanner into an Augmented Reality Price Comparison Tool.
• PropertyGuru launches its own ‘Lens’ visual search tool: Tech in Asia.
• Tommy Hilfiger’s image recognition app drives new web traffic and engagement
• IKEA launches a Pinterest-embedded catalog.
• YouTube launches AR for cosmetics.

The latest development in the industry is “multimodal search”, in which text, images, and videos can be used simultaneously to form a query.

Amazon, Myntra and Meesho are the top e-com giants that are using visual search technology in their apps in India.

Wrapping Up

Nearly 60 years later, the future of computer vision appears to be full of promise and the potential applications and consequences are vast.

Some of which are even based on science fiction.

At this time, the technologies powering computer vision have finally begun to take hold of our dreams of their applications - self-driving cars, rapid medical diagnostics, instantaneous checkouts, and more.

To make real sense of what the future might hold, we first need to trace the evolution of computer vision and dive into its real-world applications today that are already improving people's daily lives.

Visual Search Resources

• Pinterest engineering.
• Alibaba engineering: Visual search at Alibaba
• The future of visual search. Econsultancy
• Visual Search 2018: eMarketer.
• Drive conversions with visual search.
• Econsultancy guide to visual search (Download).
• Visual search on Amazon guide: Part 1
• Visual search on Amazon guide: Part 2