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Google Taking A Leap Forward By Identifying Landmarks

googleTechnology is advancing faster than you can think — the web, social networking, blogging has helped people to find information, communicate and interact with each other faster and better than ever. Now Google is taking a leap forward in the age of search through a new technology that allows landmark recognition. Google describes it as follows:

How did we do it? It wasn’t easy. For starters, where do you find a good list of thousands of landmarks? Even if you have that list, where do you get the pictures to develop visual representations of the locations? And how do you pull that source material together in a coherent model that actually works, is fast, and can process an enormous corpus of data? Think about all the different photographs of the Golden Gate Bridge you’ve seen — the different perspectives, lighting conditions and image qualities. Recognizing a landmark can be difficult for a human, let alone a computer.

Our research builds on the vast number of images on the web, the ability to search those images, and advances in object recognition and clustering techniques. First, we generated a list of landmarks relying on two sources: 40 million GPS-tagged photos (from Picasa and Panoramio) and online tour guide webpages. Next, we found candidate images for each landmark using these sources and Google Image Search, which we then “pruned” using efficient image matching and unsupervised clustering techniques. Finally, we developed a highly efficient indexing system for fast image recognition. The following image provides a visual representation of the resulting clustered recognition model:

google recognition

As you can see, it’s a research paper and not a new product yet (don’t be disappointed), however it might become a reality in time to come. This technology currently enables computers to quickly and efficiently identify images of more than 50,000 landmarks from all over the world with 80% accuracy. If Google can jack it up to 90-95%, then it will be the next big thing since text search. Maybe next on the list: video recognition?


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