Google Image Search

Basic information

How to use this tool for use/reuse assessment

Google 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. Google Reverse Image Search returns results in a search engine results format, including the website title, image resolution size, image thumbnail, and a brief description of the website. 

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

Strengths

  • Results from Google Image Search can act as data that practitioners can analyze to better understand what users do with digital objects in their collections. Google Image Search and other reverse image search engines can serve as one important piece to a multi-step assessment process.
  • A comparison of reverse image search engines suggests that the index that Google Image Search uses to query images is more extensive than other image search tools (specifically TinEye and Yandex). This index yields results for a wider range of images, not just those that are most popular among various user types. 

Weaknesses

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

  • Google Image Search, like other other popular reverse image search engines, cannot access content outside of HTML. PDF documents, audio, and video files placed on websites are not queried by Google Image Search.

  • Google Image Search, like other popular reverse image search engines, 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 MatchEngine is a fee-based reverse image search service which performs batch querying.

  • Google does not make product documentation available for the public. It is unclear how the search algorithm operates.

Additional resources

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 reverse image search 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 reverse image 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 search data and found that image use/reuse was driven by its subject matter. They also “triangulated” their 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.

  • Case Study: Assessing the reuse of library digital collection using reverse image search
    Reilly and Thompson employed Google Image Search to identify instances of selected images from the Library of Congress’s Teaching with Primary Resources shared on external web pages. Compiling a data set of over 1,400 URLs, Reilly and Thompson categorized user and use/reuse types and found that the “Personal” user type and “Social Media” and “Popular Culture Research” use/reuse types were the most prominent. Reilly and Thompson concluded that reverse image search could be used alongside other methods and analytical approaches to assess digital library collections.

    Reilly, M., & Thompson, S. (2017). Reverse image lookup: assessing digital library users and reuses. Journal of Web Librarianship, 11(1), 56-68.

Additional resources

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.

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