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admin | Category: Yellow Pages Reverse Phone Lookup | 30.10.2013
This is a place devoted to giving you deeper insight into the news, trends, people and technology behind Bing. This month, we released a new Bing image search experience designed to help customers be inspired, learn more, and do more with image search. The first generation of image search relied on similar principles to web search, where content is analyzed based on URLs (Figure 1). Fortunately, modern Image Understanding techniques create a big opportunity to bring all the metadata together for exact or near duplicate image content that is currently fragmented across many URLs.
Passionate curators painstakingly create topical image collections reflecting their unique style on Pinterest, Tumblr, Etsy, etc. Another user has a collections of everyday things in green, including home furnishing, shoes, dresses etc.
With these connections made through the Image Graph, we’ve connected our customers to others who share interest in similar decorating styles. In first generation Image Search experiences, the user’s own search term is often the only description available for the images returned. A good caption is a snippet that helps you understand an image and provides a link to the webpage where you can continue your research. We were able to achieve this at scale by utilizing knowledge of user interaction with the content and applying image understanding models to determine key concepts within the image. As you are searching and looking at this set or other sets we are able to suggest multiple shopping sources where this set is available (beta). Most image results in these scenarios are not from e-commerce sites, which can cause a lot of frustration. The Image Graph has the ability to easily do a reverse look-up of image content in near real-time. After installing the Bing Image Match extension, go to any website and hover over any image. You can try a reverse lookup of images on these pages: rustic wagon coffee table, riding boots knee high socks, chicken parmesan with pasta, capitol reef national parks. The evolution of the Image Graph has come a long way, from URL graph, to content graph, to knowledge graph. Creating a Named RangeA named range is an easy way to refer to a range of data in a formula. Opening the VLOOKUP Dialog BoxAlthough it is possible to just type our lookup formula directly into a cell in a worksheet, many people find it difficult to keep the syntax straight - especially for a complex formula such as the one we are using in this tutorial.An alternative, in this case, is to use the VLOOKUP dialog box. Tutorial StepsClick on the lookup_value line in the dialog box Click on cell D2 to add this cell reference to the lookup_value line. We discover billions of images on the Internet, but understanding the searches and providing helpful information for each image is an enormous challenge.


As illustrated in the chart below, images are often duplicated hundreds – even thousands of times. Bing seizes this opportunity by analyzing every image’s content and context and constructs an advanced graph clustering similar images and all their associated metadata together. At this scale, understanding the Image Graph based only on image content by computation is very costly. Our team mines through each of these sources to generate datasets in billions of content clusters to support each of these user goals.
In 2013, we enabled search within these collections for the first time (blog), but collections remained independent from one another and from individual images.
If one image appears in a few different collections, these collections are connected with each other. When our users explore an image in a collection, we can now show other collections containing this image.
If our customer likes this decoration style, we can offer insight through others who collected the same image. This user has collected quite a few ideas for shades using green color, the same attractive color in the image.
People can then also discover new content related to decoration ideas and dressing styles that they are likely to be interested in. We challenged ourselves to identify a minimal and human-readable set of terms that can identify the key concept in the image. We were able rank all the candidate captions based on relevance to the image, readability and importance to users.
With our quest to help users do more with Bing Image Search, we connect the dots to help our customers map one image to multiple shopping sources where the product can be found for sale and enable deal shopping. Based on the image search graph, we can find various shopping sites selling the same product by flagging where the same image content appears in different shopping pages.
It’s inconvenient and difficult to open an image search session and sometimes impossible to find the same image. With the reverse lookup, all knowledge from the Image Graph was exposed without you having to do any searching. In this blog post, my colleagues Arun Sacheti and Eason Wang provide a deeper look at how we built the image graph that allows us to automatically derive useful descriptions, captions and actions for images in our new search experience.
For instance, a product can be sold on different commerce sites, but be represented by the same image. In duplication cases, the image content is the same, but each page has unique document text and query associations that place the image in context.
This graph, which is pivoted on content within the image rather than URLs, is what we refer to as the Image Graph and is the foundational building block of our new experience.


Furthermore, the graph is updated constantly to keep up with millions of new images added every day. Examples include Best Representative Query (BRQ), Captions, Related Collections, Shopping Sources and More Sizes. The challenge we had was to identify the most useful snippet – a needle in a haystack. You can even see more visually and semantically similar images in the filmstrip below the image through BRQ. We are committed to building out the Image Graph, and sharing its knowledge with you through Bing and other Microsoft experiences. A photo of a celebrity can appear in many places on the web including news sites, blogs or as part of tweets. Alterations to otherwise identical images, such as cropped photos or photo edits, as seen in the earlier example, pose additional challenges to image search.
Building on technology developed in collaboration with Microsoft Research, the Bing Team has been able to build an efficient and scalable graph with 100s of billions of nodes and counting.
The algorithm has been applied to billions of images and the BRQ is preserved in the Image Graph. Researchers in computer vision from Microsoft Research and engineers from Bing have been working on this question for many years. We will continue to seek out ways to help our users be inspired, learn more and do more with Bing Image Search. The same image content appears in different sizes with small visual differences as illustrated below. Our customers would be forced to visit and analyze each of these web documents to learn about the content within the image. In Bing Image Search, the BRQ is displayed on the hover of search result as well as in the new image viewing experience. The first image and the second one are about the same but have differences in size, cropping and text. The BRQ is designed to be search-friendly, so you are one click away from learning more about the key concept for each image result.



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