Visual Search for the Visual Web

Source: Read Write Web, Jan 2014,

Modern search engines have been developed to identify textual keywords, so the concept of search by image is a major paradigm shift. Even Google relies on adjacent text content in order to identify images (and reverse image search just matches like pixels). 

For a while, Pinterest’s internal search engine was dependent on textual cues like alt text, words in the image link, and the user’s image description, to identify photos. 

According to Jing and Liu, Visual Graph combines big data elements with detailed individual image analysis, or as their latest post says, “Our approach is to combine the state-of-the-art machine vision tools, such as object recognition (e.g. shoes, faces), with large-scale distributed search and machine learning infrastructures.”

Pinterest is already the Visual Web’s most notable search engine—just not a very good one. So far, it relies too much on textual and user created context. But this latest acquisition indicates Pinterest’s eagerness to change that. 

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