TinEye Reverse Image Search

Basic information

How to use this tool for use/reuse assessment

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. 

Ethical guidelines

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.

Additional guidelines for responsible practice

None known.


  • Results from TinEye Reverse Image Search can act as data that practitioners can analyze to better understand what users do with digital objects in their collections. TinEye and other reverse image search engines can serve as one important piece to a multi-step assessment process. 

  • TinEye makes product documentation, FAQs, and written tutorials, including information about its search API, available to practitioners. 


  • TinEye Reverse Image Search, like other popular reverse image search engines, is not always 100% accurate. This process may return false positive results.  

  • TinEye Reverse Image Search cannot access content outside of HTML. PDF documents, audio, and video files placed on websites are not queried by TinEye.

  • TinEye Reverse Image Search does not allow for batch searching. Practitioners must complete a manual search, one-at-a-time. This can be time consuming and suggests that this approach would not scale to assess a practitioner’s entire digital collections. However, TinEye does offer a fee-based service, MatchEngine, which performs batch querying.

  • Practitioners should note that TinEye Reverse Image Search alone will not provide the analysis needed to perform a use/reuse assessment. Instead, it can generate a list of data points, which then the practitioner will need to explore further, using a variety of possible methods, to assess the presence and extent of use/reuse. 

  • A comparison of reverse image search engines suggests that the index that TinEye uses to query images is not as vast or extensive as other image search tools (specifically Google Image Search) so users may find the best success when searching for popular, highly shared images in TinEye. 

Alternative tools

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. 

Real world examples

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.

Additional resources

Adrakatti, A. F., Wodeyar, R. S., & Mulla, K. R. (2016). Search by image: a novel approach to content based image retrieval systemInternational Journal of Library Science, 14(3), 41-47.

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