#Google photo search upgrade
#Google photo search full
#Google photo search software

Since we were working on search across photos, we needed an appropriate label set. The first is our label and training set and how it compares to that used in the ImageNet Large Scale Visual Recognition competition. We feel it would be interesting to the research community to discuss some of the unique aspects of the system we built and some qualitative observations we had while testing the system. Second, new training techniques have made it possible to train the large deep neural networks necessary for successful image recognition. Ten years ago, running neural networks of this complexity would have been a momentous task even on a single image - now we are able to run them on billions of images. First, bigger and faster computers have made it feasible to train larger neural networks with much larger data.

What is different is that both computers and algorithms have improved significantly.

#Google photo search software

Why the success now? What is new? Some things are unchanged: we still use convolutional neural networks - originally developed in the late 1990s by Professor Yann LeCun in the context of software for reading handwritten letters and digits. We took cutting edge research straight out of an academic research lab and launched it, in just a little over six months.

#Google photo search full

We acquired the rights to the technology and went full speed ahead adapting it to run at large scale on Google’s computers. We knew we had found what we needed to make photo searching easier for people using Google.

When we evaluated these models, we were impressed on our test set we saw double the average precision when compared to other approaches we had tried. We built and trained models similar to those from the winning team using software infrastructure for training large-scale neural networks developed at Google in a group started by Jeff Dean and Andrew Ng. The winning team was from Professor Geoffrey Hinton’s group at the University of Toronto. A system which used deep learning and convolutional neural networks easily beat out more traditional approaches in the ImageNet computer vision competition designed to test image understanding. This past October the state of the art seemed to move things a bit closer to toddler performance. For other classes of objects, this is a daunting task, because the average toddler is better at understanding what is in a photo than the world’s most powerful computers running state of the art algorithms. There are some things a computer can do well, like recognize rigid objects and handwritten digits. This makes it harder for a computer to identify and categorize what is in a photo. However, in the case of photos, there is typically little or no information beyond the pixels in the images themselves. In Image Search there are many pieces of information which can be used for ranking images, for example text from the web or the image filename.

This is powered by computer vision and machine learning technology, which uses the visual content of an image to generate searchable tags for photos combined with other sources like text tags and EXIF metadata to enable search across thousands of concepts like a flower, food, car, jet ski, or turtle.įor many years Google has offered Image Search over web images however, searching across photos represents a difficult new challenge.

#Google photo search upgrade

Last month at Google I/O, we showed a major upgrade to the photos experience: you can now easily search your own photos without having to manually label each and every one of them. Posted by Chuck Rosenberg, Image Search Team