The problem of finding information in large volumes of imagery is a challenging one, with few good solutions. While most search engines allow users to find information in collections of text quite efficiently, there is a lack of similar solutions when it comes to searching for imagery. The problem is computers aren’t able to interpret imagery very well. They can’t deal with novelty, variability, or exploit contextual information and prior knowledge to the extent that humans can.

Unfortunately, most manual image analysis tools currently in use are inefficient - tapping into slow and deliberate cognitive processes. Most image search and analysis tools do not exploit the reliable split second perceptual judgments that people make continuously - think of returning a tennis serve, hitting a baseball, or reacting to an obstacle on the highway while driving. The question we have been asking is whether we can tap into these perceptual judgments, in order to find visual information within large image sets efficiently…