  Much computer vision research has focused on natural images, but technical
documents typically consist of abstract images, such as charts, drawings,
diagrams, and schematics. How well do general web search engines discover
abstract images? Recent advancements in computer vision and machine learning
have led to the rise of reverse image search engines. Where conventional search
engines accept a text query and return a set of document results, including
images, a reverse image search accepts an image as a query and returns a set of
images as results. This paper evaluates how well common reverse image search
engines discover abstract images. We conducted an experiment leveraging images
from Wikimedia Commons, a website known to be well indexed by Baidu, Bing,
Google, and Yandex. We measure how difficult an image is to find again
(retrievability), what percentage of images returned are relevant (precision),
and the average number of results a visitor must review before finding the
submitted image (mean reciprocal rank). When trying to discover the same image
again among similar images, Yandex performs best. When searching for pages
containing a specific image, Google and Yandex outperform the others when
discovering photographs with precision scores ranging from 0.8191 to 0.8297,
respectively. In both of these cases, Google and Yandex perform better with
natural images than with abstract ones achieving a difference in retrievability
as high as 54\% between images in these categories. These results affect anyone
applying common web search engines to search for technical documents that use
abstract images.
