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Much computer vision research has focused on natural images, but technicaldocuments typically consist of abstract images, such as charts, drawings,diagrams, and schematics. How well do general web search engines discoverabstract images? Recent advancements in computer vision and machine learninghave led to the rise of reverse image search engines. Where conventional searchengines accept a text query and return a set of document results, includingimages, a reverse image search accepts an image as a query and returns a set ofimages as results. This paper evaluates how well common reverse image searchengines discover abstract images. We conducted an experiment leveraging imagesfrom 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 thesubmitted image (mean reciprocal rank). When trying to discover the same imageagain among similar images, Yandex performs best. When searching for pagescontaining a specific image, Google and Yandex outperform the others whendiscovering photographs with precision scores ranging from 0.8191 to 0.8297,respectively. In both of these cases, Google and Yandex perform better withnatural images than with abstract ones achieving a difference in retrievabilityas high as 54\% between images in these categories. These results affect anyoneapplying common web search engines to search for technical documents that useabstract images.

Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine