On October 30, 2020, the Bing Search APIs moved from Cognitive Services to Bing Search Services. This documentation is provided for reference only. For updated documentation, see the Bing search API documentation. For instructions on creating new Azure resources for Bing search, see Create a Bing Search resource through the Azure Marketplace.
The Bing Visual Search API returns insights for an image. You can either upload an image or provide a URL to one. Insights are visually similar images, shopping sources, webpages that include the image, and more. Insights returned by the Bing Visual Search API are similar to ones shown on Bing.com/images.
Today, we shall discuss the clear-cut difference between a visual and an image search and the tools to utilize them. If you want to protect your images, want to search for any particular picture in seconds, or need to fact-check an origin/image, then read this post till the end!
Pinterest and Amazon are the primary visual search engines now. According to the latest news, Microsoft has also revealed that it has created impressive computer vision abilities for its Bing search engine.
Visual search technology has the power to transform the method we communicate with the world around us. Our society is now managed by the visual, so it looks natural to utilize an image to search. Behind all, when we buy offline, we unusually begin with the text. Visual search is bringing that insight of visible results to the online world.
Furthermore, people often require a modern look, outfit, or theme rather than a particular item. The visual search technology supports matching these objects together based on beautiful links in a custom text that has never been capable of obtaining.
Reverse Image search by SearchEngineReports.net is a free online utility to fetch similar and identical images in one go! It is a fantastic feature that utilizes Reverse Photos search and lets users explore similar images by dropping an image or image URL. The web world achieves this by investigating the offered picture and building a mathematical model for applying advanced algorithms.
Although one of the lesser-discussed forms of search, visual search has been around for a number of years and is built into several popular search engines and social media platforms, including Pinterest, Bing, Snapchat, Amazon, and, of course, Google.
This technology is particularly useful for eCommerce stores and brands, and with the implementation of well-optimized content, they could stand the chance of being the returned search result for a user.
Although both visual and image search is based around imagery, the crucial difference lies in the fact people use words to conduct an image search, whereas, with visual search, a user uses an image to conduct the search.
As you are probably aware, image search has been in the public consciousness for nearly 20 years. In fact, Google introduced the search format way back in July 2001, due to the fact that the search engine could not handle the number of people searching for an image of Jennifer Lopez in a particular green dress.
Bing Visual Search technology is a very different visual search tool from Pinterest Lens, due to the fact that it works to provide people with information as well as products; pretty much like the Bing search engine itself.
Here, Bing allows developers to tell the search engine what information people should gain from a particular image. So, for instance, if Bing Visual Search leads someone to a particular product on your site, a developer has the ability to define what action(s) should be offered to the user.
Last but certainly not least, Google Lens was announced and launched at Google I/O in 2017 and has quickly become the most popular visual search platform in the world because it features advanced visual search capabilities.
In October 2018, Google Lens was incorporated into Google Image Search, and in 2019 a study found that its image recognition technology was more accurate than a number of other major visual search platforms.
With 35% of marketers planning to optimize for visual search in the future, getting ahead of the competition is better done early rather than late. But what are the primary benefits of optimizing for visual search?
With no less than 60% of Generation Z now discovering brands solely through social applications and 69% them looking to purchase directly off the back of the platforms, there has never been a better time to get your brand discovered outside of traditional search fields.
With properly optimized content through visual search, you can find your site interacting with people that have already made their minds up about whether they want to make a purchase, especially if they are using Pinterest Lens or StyleSnap.
Lastly, and probably one of the most important benefits of visual search is that, after some investment, sites can look forward to the opportunity of vastly increased revenues. According to Gartner, early adopters and optimizers of both visual and voice search could find their coffers increased by as much as 30% by 2021.
As we have discovered, there are numerous benefits for getting involved in visual search, but the time has now come to discuss how to appear in visual search and the best practices that sites should adhere to.
When adding any kind of content to a website, it is important to provide search engines with as much information as possible. One way to do this is through structured data for images, which will also help your site appear in rich snippets in Google.
Alternative text is also read by search engines to help them understand the context and meaning of a picture. People who use screen readers also need alt text so that they too understand the context of an image.
Although the above recommendations are based purely around images, it is important to remember that like anything in SEO, there are many associated elements that should be considered to appear in visual search results, including:
As Google and other platforms push the boundary of what search is and what it can be, it is integral that site owners and marketers alike plan for the future of search, which at the moment, is wrapped in sensory search in all its forms.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and run Elasticsearch cost-effectively at scale. Amazon ES offers k-Nearest Neighbor (KNN) search, which can enhance search in similar use cases such as product recommendations, fraud detection, and image, video, and semantic document retrieval. Built using the lightweight and efficient Non-Metric Space Library (NMSLIB), KNN enables high-scale, low-latency, nearest neighbor search on billions of documents across thousands of dimensions with the same ease as running any regular Elasticsearch query.
In this step, from each image you extract 2,048 feature vectors from a pre-trained Resnet50 model hosted in Amazon SageMaker. Each vector is stored to a KNN index in an Amazon ES domain. For this use case, you use images from FEIDEGGER, a Zalando research dataset consisting of 8,732 high-resolution fashion images. The following screenshot illustrates the workflow for creating KNN index.
In this post, we showed you how to create an ML-based visual search application using Amazon SageMaker and the Amazon ES KNN index. You used a pre-trained Resnet50 model trained on an ImageNet dataset. However, you can also use other pre-trained models, such as VGG, Inception, and MobileNet, and fine-tune with your own dataset.
Today, people are taking pictures of everything, not just beautiful sceneries or mementos of their adventures, but stuff they need to remember or tasks they need to do. Visual search will increasingly help turn those images into actual tasks. Take a photo of a business card and automatically add the contact details to my address book. Or take a photo of my written shopping list and add the items to the shopping cart of my favorite supermarket. Everything is possible.
While visual search uses visuals as a starting point, image search is different. Image search has been around forever. A classic image search starts with a typed search prompt in a search field and leads to a SERP that shows a number of images that match that specific search. These images can be narrowed down by selecting smart filters from the menu bar.
People have been talking about visual search for a long time, but over the past couple of years it has really come into its own. Very powerful smartphones, increasingly smart artificial intelligence and consumer interest drive the growth of this exciting development. But how does visual search work?
Visual search is powered by computer vision and trained by machine learning. Computer vision can be described as the technology that lets computers see. Not only that, it makes computers understand what they see and to make them do something useful with that knowledge. In a sense, computer vision tries to get machines to understand the world we as humans see.
Visual search can be used for a lot of things, like helping you discover landmarks in a strange city, helping you increase productivity or find the beautiful pair of shoes that fits perfectly with that new dress you bought. It can also help you identify stuff like plants and animals and teach you how to do a particular chore. Who knows what else?
Facebook, for instance, works on building an AI powered version of their Marketplace. They even purchased a visual search technology start up called GrokStyle that could drive that development. Apple also bought several companies active in the visual search space, mostly to improve their photo apps, while their ARKit developer program has very interesting options for working with visuals. Both Snapchat and Instagram let you buy stuff on Amazon by pointing your camera at an object. 2b1af7f3a8