How Microsoft Beat Google at Understanding Images with Machine Learning
And how Redmond beat everyone else, for that matter…
Mary Branscombe | December 28, 2015
Elon Musk is only the latest investor in artificial intelligence, helping to fund a big-name roster of researchers who promise to change the field. Meanwhile, Microsoft Research is actually doing it, by combining the popular deep networks that everyone from Google to Facebook is also using for machine learning with other mathematical techniques, and beating them all in the latest round of the annual ImageNet image recognition competition.
ImageNet tests how well computers can recognise which of 1,000 different categories the 100,000 test images belong in, and where in the photo the object being recognised is.
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| Source: http://www.pcworld.com/article/2849838/microsoft-five-other-groups-race-toward-automated-image-captioning.html |
<more at http://www.techradar.com/news/world-of-tech/how-microsoft-beat-google-at-understanding-images-with-machine-learning-1311683; related links: http://www.pcworld.com/article/2849838/microsoft-five-other-groups-race-toward-automated-image-captioning.html (Microsoft, five other groups race toward automated image captioning. November 19, 2014) and http://www.image-net.org/ (ImageNet website. [About: Overview. Welcome to the ImageNet project! ImageNet is an ongoing research effort to provide researchers around the world an easily accessible image database. On this page, you will find some useful information about the database, the ImageNet community, and the background of this project. Please feel free to contact us if you have comments or questions. We'd love to hear from researchers on ideas to improve ImageNet. What is ImageNet? ImageNet is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy.])>
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