Cordelia Schmid’s algorithms set the stage for fast image searches on the internet

When Cordelia Schmid wrote her groundbreaking dissertation in 1996, image classification was still in its infancy: “Back in these days, the systems were only capable of recognising simple geometric shapes such as circles, triangles, or squares, and this only against a uniform background.” Schmid substantially improved image recognition by focusing on distinctive features of an image, so-called local image descriptors. These image descriptors represent the spatial dimensions of the object displayed. Image descriptors enable computers to identify an object even when it is partially hidden or displayed from ­a different perspective. In other words, a system em­ploying image descriptors will recognise the Eiffel Tower even if it is photographed from below from a short distance, or slanted from the side, or from a long distance when a tree blocks the view of parts of the tower. With this innovation, Schmid laid the foundation for today’s search engines to search millions of images on the internet within seconds.

After the turn of the millennium, automatic image recognition made great advances and engendered many novel approaches. During this period, Cordelia Schmid developed benchmark tests that made it possible to determine the most effective of these numerous new methods. Among the test criteria was—in addition to a high success rate in finding the desired images—that the processing speed be as fast as possible.

In 2006, Schmid developed another standard procedure for image recognition: “spatial pyramid matching”. This approach divides images into smaller and smaller sections, which makes the process of grasping spatial structures more flexible. “We were now able to clearly distinguish between categories such as ‘bedroom’ and ‘living room,’ and image content such as a beach scene was recog­nised at first sight,” says Schmid. 

With this innovation, Schmid laid the foundation for today’s search ­engines to search millions of images on the internet within seconds.

Image recognition involves complex calculations. Different approaches have to be weighed against each other. ­