One of the greatest challenges visual artists face is tracking down other uses of their work online. Whether it’s a search aimed at stopping infringements or simply understanding how your work is being used, finding visual works is a tricky matter.
The reason for the problem is that most search tools, including image search tools, don’t actually look at image, they look at the text around it. So unless the title of your work or the file name remain the same, there isn’t much hope for spotting a duplicate via traditional means.
But, while Tineye’s matching technology is and always has been great, it’s been limited by Tineye’s rather small database. While that database has grown over 2x since I first wrote about the service (currently at just over 2 billion images), it hasn’t kept pace with the images being uploaded to the Web (Photobucket alone has 9.5 billion images).
However, last year Google entered into the fray, adding the ability to search for images by uploading or linking to another one, as with Tineye. The feature, which was initially an extension of Google’s previous Similar Image Search function, wasn’t very successful at first. However, over time, it appears Google has gone a long way to improving the tool as, in a recent spate of tests, it drastically outperformed Tineye in finding matching images consistently.
So, for artists looking to find their images on the Web, there seems to be a new king in town and it’s the same one authors have been using for years.
How to Use Google Search By Image
However, barring that, you can simply visit Google Image Search and click the camera icon in the search bar.
That will open up the window that prompts you to either upload an image from your computer or provide the URL for one already online.
After you submit your image, Google will present a set of results. However, rather than being a “grid” like a regular Google Image Search, the results are ordred, first by exact matches and then by similar ones.
For example, I uploaded my recent image of the cover of Ocean’s Donkey Kong unlicensed port (from my recent article on video game plagiarism
In some cases, if there are multiple versions of the images but at different sizes, Google may suggest you look for alternate sizes of the image, as it did with the Limbo of the Lost cover from the same article.
If so it’s worth clicking the link to get a good breakdown of the other sizes (and places) the image appears. However, the real results, especially for images that have been widely copied, are below and you can see them by going through the various pages, as you would with a regular Google search.
But while Google Search By Image is cetainly easy to use, how well does it stack up against Tineye? The answer, is very well.
To test the two services head-to-head, I decided to have them both look for five different images used in recent articles on Plagiarism Today. These images are all either freely-available stock photos or are widely-used cover art for video games or records.
With that in mind, here’s the results of the tests:
Test 1: Angry Farm Image
Tineye Results: 1
Google Results: 558 (about)
Test 2: Generic Chart Image
Tineye Results: 4
Google Results: 555 (about)
Test 3: Facepalm Image
Tineye Results: 21
Google Results: 850 (about)
Test 4: Skull on Grave Image
Tineye Results: 1
Google Results: 3 (Not counting matching “Very Similar” results)
Note: Google’s “Very Similar” results were useless in this case as it just found other black and white photos without much regard for things that looked like the original.
Test 5: Bing Crosby White Christmas
Tineye Results: 173
Google Results: 883 (about)
Please note that the greatest limitation of this test is that I had to rely on both search engines to self-report how many matching images they had. However, I checked several pages of results with each test to make sure that the results were as accurate as possible.
Clearly though, the winner is Google, which found, in many cases, over 100x more matching images than Tineye. My suspicion is that, while Tineye’s matching algorithm is better (much fewer false positives), Google’s large database simply makes up the ground and then some, making it a much more valuable tool for image detection.
However, this doesn’t mean that Google’s perfect, there are still a few concerns and problems I have with it.
Limitations of Google
The biggest problem with Google is that, currently, there is no way to do a bulk search for a lot of images nor is there a way to do a recurring search. Though Google has an API for its Google Image Search, it doesn’t appear to work with Search by Image. Likewise, Google Alerts doesn’t allow you to create an alert for a Google Image Search at all, text or by image.
In short, there are no tools to make such searches easier and there aren’t likely to be any in the near future.
Meanwhile, Tineye has a very robust and well-established API that enables toe construction of just such tools.
Still, given how simple it is to use Google Search By Image, even with having to do the searches by hand, it’s still faster and easier than most methods, it’s still free and, in the end, just more effective, even more so than many paid-for tools.
There’s little reason not to integrate Google Search By Image into your checks and to use it at least some in your searches.
When Google first launched this product, I tested it and found it to be less-than-useful. The algorithm was too flawed (based on the earlier and more limited “similar” search feature) and the number of false positives simply too high. Clearly, Google has made some great strides in the last six months and pushed this service to the point where it’s database and accuracy combine to make it the most useful image search tool available to the public, especially for free.
In the end, while I like Tineye as a company and as an offering, their database is too small and too limited to compete right now. Hopefully though, this competition will motivate both companies to improve their offerings and create a set of solutions that make things much easier on photographers and artists everywhere.