Google Image Classification and Landmarks

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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 there 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 very different 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 followup to the other.

The first of these papers, Google-Landmarks: A New Dataset and Challenge for Landmark Recognition, starts out 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. In order to continue advancing the state of the art in computer vision, many researchers are now putting more focus on fine-grained and instance-level recognition problems – instead of recognizing general entities such as buildings, mountains and (of course) cats, many are designing machine learning algorithms capable of…

Read the rest.

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