(Source: University of California, San Diego)
Researchers at the UC San Diego have put Google's "Sets" widget to good use by giving computers the ability to identify objects by context
Google's free widgets have been used in many applications other than those Google initially anticipated. For example, some organizations use Google Maps to pinpoint locations of certain buildings relating to that group's business. Even Google has applied mapping applications like Picasa to identify the location of the setting of a user's digital photo. Then there are the more advanced applications such as giving a computer the ability to identify objects in a picture based on context.
Researchers at the University of California, San Diego and the University of California, Los Angeles announced a breakthrough of "common sense" by creating an algorithm that uses a not-so-used Google widget called Google Sets. For those unfamiliar with Google Sets, the web application accepts up to 5 words by default. These words can be as related or unrelated as the user would like them to be. When the user clicks either "Large Set" (which returns greater than 15 items) or "Small Set" (which returns 15 items or fewer) Google Sets will produce a page of results with words that can relate to a combination of the words submitted by the user.
By utilizing Google Sets, the UCSD and UCLA researchers have been able to take a digital photograph of a woman playing tennis and give a computer the ability to identify the specific objects in the photo (PDF) such as a tennis ball, tennis racket, tennis court, and the person playing tennis.
In the scene of a tennis match, four objects are detected and categorized: “Tennis court”, “Person”, “Tennis Racket”, and “Lemon”. Using a categorization system without a semantic context module, these labels would be final; however, in context, one of these labels is not satisfactory. Namely, the object labeled “Lemon”, with an appearance very similar to a “Tennis Ball” is probably mis-labeled, due to the ambiguity in visual appearance. By enforcing semantic contextual constraints, provided by an oracle, the label of the yellow blob changes to “Tennis Ball”, as this label better fits in context with other labels more precisely.
In this case, the oracle would be Google Sets which would help pinpoint the theme of the photograph and choose what it believes the round yellow shape is based on that theme.
Though this algorithm used in conjunction with Google Sets may identify objects in a simple scene with a clean background like the image to the right, more complex scenes with busier backgrounds may prove to be difficult for now. Still, the use of Google Sets and other algorithms which share the same idea seems to be the right path to creating highly advanced image identification algorithms.
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