Image Classification and Landmarks at Google

Sharing is caring!

Washington Monument

Image Classification in the past

Back in 2008, I was writing about how a search engine might learn from photo databases like Flickr, and how people label images in a post I wrote called, Community Tagging and Ranking in Images of Landmarks

In another post that covers the Flickr image classification Landmark work, Faces and Landmarks: Two Steps Towards Smarter Image Searches, I mentioned part of what the Yahoo study uncovered:

Using automatically generated location data and software that can cluster together similar images to learn about images again goes beyond just looking at the words associated with pictures to learn what they are about.

That is using metadata from images in an image collection, which is a very different image classification approach from what Google is doing in this post about identifying landmarks in the post, How Google May Interpret Queries Based on Locations and Entities (Tested), where it might identify landmarks based upon a knowledge of their actual location.

More Recent Image Classification of Landmarks

I mention those earlier posts because I wanted to share what I had written about landmarks before pointing to more recent studies from Google about how they might recognize landmarks a year apart from each other, with one being a follow-up to the other.

The first of these papers, Google-Landmarks: A New Dataset and Challenge for Landmark Recognition, start by telling us about a problem that needs solving:

Image classification technology has shown remarkable improvement over the past few years, exemplified in part by the Imagenet classification challenge, where error rates continue to drop substantially every year. Many researchers are now focusing on fine-grained and instance-level recognition problems to continue advancing state of the art in computer vision. Instead of recognizing general entities such as buildings, mountains, and (of course) cats, many are designing machine learning algorithms capable of identifying the Eiffel Tower, Mount Fuji, or Persian cats. However, a significant obstacle for research in this area has been the lack of large annotated datasets.

A year later, Google worked to improve the dataset that was being used for image classification when identifying landmarks and updated the dataset that they had created the year before, as they tell us in,Announcing Google-Landmarks-v2: An Improved Dataset for Landmark Recognition & Retrieval Part of the effort behind that work came from getting a lot of help as described in the blog post announcing it:

A particular problem in preparing Google-Landmarks-v2 was the generation of instance labels for the landmarks represented since annotators can’t recognize all of the hundreds of thousands of landmarks that could potentially be present in a given photo. Our solution to this problem was to crowdsource the landmark labeling through the efforts of a world-spanning community of hobby photographers, each familiar with the landmarks in their region.

Google Patent for Image Classification when Identifying Landmarks in Image Collections

image classification

Google was recently granted a patent that focuses on identifying popular landmarks in large digital image collections. Considering Google operates Google photos, which makes a lot of sense. The landmark identification efforts at Flickr sound a little similar to this effort on Google’s part. The patent does target a specific problem which it tells us is:

However, no known system can automatically extract information such as the most popular tourist destinations from these large collections. As numerous new photographs are added to these digital image collections, it may not be feasible for users to manually label the photographs to increase the usefulness of those digital image collections. Therefore, what is needed are systems and methods that can automatically identify and label popular landmarks in large digital image collections.

Some of it does sound similar to the Flickr efforts where it talks about working to populate and update “a database of images of landmarks including geo-clustering geo-tagged images according to geographic proximity to generate one or more geo-clusters, and visual-clustering the one or more geo-clusters according to image similarity to generate one or more visual clusters.”

How might this play into image classification and search involving landmarks?

The patent describes how it could fit into searches, with the following steps:

  • Enhancing user queries to retrieve images of landmarks, including the stages of receiving a user query
  • Identifying one or more trigger words in the user query
  • Selecting one or more corresponding tags from a landmark database corresponding to the one or more trigger words
  • Supplementing the user query with the one or more corresponding tags, generating a supplemented user query

Trigger words appearing in queries are interesting.

The image classification patent also tells us that it could also involve a method of automatically tagging a new digital image, which would also cover:

  • Comparing the new digital image to images in a landmark image database, wherein the landmark image database comprises visual clusters of images of one or more landmarks
  • tagging the new digital image with at least one tag based on at least one of said visual clusters

The patent is:

Automatic discovery of popular landmarks
Inventors: Fernando A. Brucher, Ulrich Buddemeier, Hartwig Adam and Hartmut Neven
Assignee: Google LLC
US Patent: 10,289,643
Granted: May 14, 2019
Filed: October 3, 2016


In one embodiment, the present invention is a method for populating and updating a database of images of landmarks, including geo-clustering geo-tagged images according to geographic proximity to generate one or more geo-clusters and visual-clustering the one or more geo-clusters according to image similarity to generate one or more visual clusters. In another embodiment, the present invention is a system for identifying landmarks from digital images, including the following components: a database of geo-tagged images, a landmark database; a geo-clustering module; and a visual clustering module. In other embodiments, the present invention may enhance user queries to retrieve images of landmarks or a method of automatically tagging a new digital image with text labels.

Even Smarter Image Classification of Landmarks?

This system appears to be capable of finding trendy landmarks in photo collections across the web and storing those in a landmark database, where it might be geo-cluster those. It’s interesting to think about this effort. If Google might use those landmark images in Image Search Results, it may not stop image classification at that point.

I recently wrote about Google Image Search Labels Becoming More Semantic? where we were told in an updated Google Patent that images were being labeled based upon an ontology related to the topics of those imageSo, forFor example, a Google image search for a landmark like The Washington Monument shows several image classification labels at the top of the results that can be clicked on if you want to narrow down the results to specific aspects of those monuments.

So, image classification may include specific monuments, and then even more narrow classifications, like having the following labels applied to the Washington Monument:

Reflecting Pool
Lincoln Memorial
Washington DC
Observation deck
National Mall

So, Google may have smarter image classification when it comes to landmarks, but it is labeling them so that they are more meaningful, too.

Last Updated May 28, 2019

Sharing is caring!

13 thoughts on “Image Classification and Landmarks at Google”

  1. Thanks for this post Bill,

    I’ve been very interested in image search patterns since Google I/O’s announcement that image search will become even more important. Really well-timed post 🙂

  2. Thanks, Jean-Christophe,

    I saw this patent and remembered the two landmark posts at the Google Blog, so thought it was a worthwhile topic to post about. I was also suprised to see Hartmut Neven
    listed as an inventor of this patent, thinking back to Google’s acquisition of nevenengineering:

    Google Acquires Neven Vision: Adding Object and Facial Recognition Mobile Technology

    The company was originally acquired to help with organizing images in Picassa, and this recent patent mentions Picassa, even though that has been shut down at Google for a few years. The picassa page says that it was shut down to allow Google to focus upon providing services under the Google Photos name. We are seeing object recognition in services such as Google Lens now, too.

  3. hello bill,
    thanks a lot for sharing these post .it is very effective and interesting to know about changes in google landmark searching.
    keep posting.

  4. Hey Bill

    Thanks for sharing great information! its about image search patterns and google changes in images search pattern!

  5. Hey Bill

    Great article once again. Whenever i need to know something new about what google is doing.I visit your website. Your website helps me in keeping up to date with all the information about SEO. Thanks you. Keep doing good work !

  6. This is the first time I came across the blog about Image Classification. Thanks for the concise summary of this topic. I appreciate your guidance

  7. Very nice post, I just stumbled for your weblog and wanted to say that I’ve really enjoyed surfing around your blog posts, as they are highly knowledgeable. I am convinced to add your website in my bookmark and I am hoping you write more often!

  8. I am reading your blog for some time I like most of your content mostly about google whenever I need to know something new about what google is doing. I visit your website. Thanks for sharing deep knowledge.

  9. Extraordinary article by and by. At whatever point I have to know something new about what google is doing.I visit your site. Your site causes me in staying up with the latest with all the data about SEO. Much appreciated you. Continue doing great work !

  10. Hi Bill, sorry for my stupid question: What’s the number in the picture (101, 103,…) means?

  11. Hi Huynhlekien,

    The drawing is one from the patent, and if you follow the link to the patent, you will see that number in the patent describing the process illustrated in the patent drawing. It has no significance beyond that at all.

Comments are closed.