Google Paper: Better Image Search Though VisualRank / Image Rank

Visual Rank Example

A Google Prototype for a Precision Image Search from the New York Times covers a new research paper (PDF format) from Google that talks about a way of ranking images based on analyzing "visual links" between them.

Image search at the major search engines today largely relies on looking at words that are used around images — on the pages that host them, in image file names, and in ALT text associated with them. No real image recognition is done by any of the majors. Search for "apples," and they haven’t actually somehow scanned the images itself to "see" if they contain pictures of apples.

The method in Google’s paper changes that. In short, a group of images retrieved for a query using traditional search methods is then further analyzed. Image recognition software finds which images in the group seem most similar to each other. It then estimates "visual hyperlinks" between them to produce a final ranking.

The last part is important. No actual hyperlinks on the web are used to rank the images, if I understand the paper correctly, other than in the first traditional retrieval process. Instead, the algorithm guesses at how the images would be linked together, with those being most similar having more virtual links to each other. As a result, the most "linked to" images are calculated to rank first.

The image above comes from the paper and shows examples of images found in a search for [mona lisa]. The lines illustrate how they are all estimated to link together, with the two in the middle (as shown in the close-up below) deemed the most relevant based on linkage:

Visual Rank Example

The New York Times article says the researchers call the method "VisualRank," though that term is not used in the actual paper, which is entitled "PageRank for Product Image Search," coming from how the method was applied to product search results as a test. The paper itself talks of Image Rank at one point, so VisualRank might be a new name the researchers are trying out.

Image recognition isn’t new or unique to Google, though this twist on using virtual hyperlinks is. For background on what others are doing, see Teaching Google To See Images from Chris Sherman last year. It covers players such as Riya. My article on Polar Rose, Polar Rose Promising Face Recognition Image Search, also provides some further background on image recognition as well as the Google Images Labeler that relies on human judgment to identify image.

For further discussion, see Techmeme.

Related Topics: Channel: Consumer | Google: Images | Search Engines: Photo & Image Search


About The Author: is a Founding Editor of Search Engine Land. He’s a widely cited authority on search engines and search marketing issues who has covered the space since 1996. Danny also serves as Chief Content Officer for Third Door Media, which publishes Search Engine Land and produces the SMX: Search Marketing Expo conference series. He has a personal blog called Daggle (and keeps his disclosures page there). He can be found on Facebook, Google + and microblogs on Twitter as @dannysullivan.

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