TinEye is an image search and recognition company. Its Reverse Image Search queries webpages for instances of an image. To complete a search, a user can select a file from their local computer (even dragging and dropping the image into the search box), enter the URL to the image file already on the web, or search using text for an image already on a website. TinEye Reverse Image Search returns results in a search engine results format. It allows users to sort results by web pages with images, similar images to the one queried, and text terms generated related to the image. TinEye Reverse Image Search also provides information on the dates in which the images were indexed, as well the filename of the images.
Practitioners should follow the practices laid out in the “Ethical considerations and guidelines for the assessment of use and reuse of digital content.” The Guidelines are meant both to inform practitioners in their decision-making, and to model for users what they can expect from those who steward digital collections.
None known.
Other reverse image search engines operate in similar ways: a practitioner submits an image to the search engine and it displays a list of results. No free reverse image search tools allow users to do batch queries of multiple images (see TinEye MatchEngine for a paid service that offers batch querying). Limited comparisons among reverse image search tool features and functions suggest that Google Image Search has a larger index in which to query. This can translate into a more diverse set of results.
Case Study: Assessing the reuse of a museum digital collection using reverse image search
Kirton and Terras leveraged RIL as one part of their study to understand how images from the National Gallery in London, UK were being shared across the web. Utilizing Google Image Search and TinEye RIL search engines, Kirton and Terras generated a data set of over 3,000 instances of digital objects being posted on the web. They conducted qualitative analysis on the RIL data and found that image use/reuse was driven by its subject matter. They also “triangulated” RIL findings with web analytics data to develop a more nuanced understanding of sharing patterns, noting that more frequently visited pages often had more frequently shared images across the web.
Kirton, I., & Terras, M. (2013, March). Where do images of art go once they go online? A reverse image Lookup study to assess the dissemination of cultural heritage.In Museums and the Web 2013, N. Proctor & R. Cherry (eds). Silver Spring, MD: Museums and the Web.
Adrakatti, A. F., Wodeyar, R. S., & Mulla, K. R. (2016). Search by image: a novel approach to content based image retrieval system. International Journal of Library Science, 14(3), 41-47.
Helping digital collections measure impact
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