IMAGE ANNOTATION - MIT

Currently computers have difficult recognizing objects in images. While practical solutions exist for a few simple classes such as human faces or cars, the more general problem of recognizing all different classes of objects in the world (e.g. guitars, bottles, telephones) remains unsolved. Computer Vision researchers are currently investigating methods that can recognize and localize thousands of different object categories in complex scenes. A key component of these algorithms is the data used to train the computers' model of each object. Current approaches use collections of images laboriously gathered by hand. Our research explores how the billions of images available on the Internet can be used to train models for object recognition. With overwhelming amounts of data, many problems can be tackled with simple algorithms. We gathered from the web 79 million images. We are using this massive dataset to train a computer to recognize objects within an image and to understand the scenes depicted in photographs.

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