Google photo search2/14/2024 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. 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
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