Technique enables computers to find visually similar images

The team — led by Alexei Efros, associate professor of computer science and robotics, and Abhinav Gupta, assistant research professor of robotics — found that its technique works on a number of visual tasks that normally confuse computers, such as matching sketches of cars with photographs of cars.

Most computerised methods for matching images focus on similarities in shapes, colours and composition. However, these methods can fail when photographs are taken in different seasons or under different lighting conditions. In addition, different domains, such as photographs, colour paintings and black-and-white sketches can also prove difficult to match together.

The uniqueness of each image was determined by looking at a very large data set of randomly selected images. The team described features that are unique as those that best discriminate one image from the rest of the random images.

By using the technique, the team believes it may be possible in many cases to match historic photos with an existing online photo. Similarly, it thinks the method could be combined with large GPS-tagged photo collections to determine the location of where a landmark painting was done.