Could a scene-recognition system help robots detect individual objects, too?

Could a scene-recognition system help robots detect individual objects, too?
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Object recognition — determining what objects are where in a digital image — is a central research topic in computer vision.

But a person looking at an image will spontaneously make a higher-level judgment about the scene as whole: It’s a kitchen, or a campsite, or a conference room. Among computer science researchers, the problem known as “scene recognition” has received relatively little attention.

Last December, at the Annual Conference on Neural Information Processing Systems, CSAIL researchers announced the compilation of the world’s largest database of images labeled according to scene type, with 7 million entries. By exploiting a machine-learning technique known as “deep learning” — which is a revival of the classic artificial-intelligence technique of neural networks — they used it to train the most successful scene-classifier yet, which was between 25 and 33 percent more accurate than its best predecessor.

At the International Conference on Learning Representations this weekend, the team will present a new paper demonstrating that, en route to learning how to recognize scenes, their system also learned how to recognize objects. The work implies that at the very least, scene-recognition and object-recognition systems could work in concert. But it also holds out the possibility that they could prove to be mutually reinforcing.

The team includes associate professor Antonio Torralba, graduate students Bolei Zhou and Aditya Khoslaa, principal research scientist Aude Oliva and visiting scientist Agata Lapedriza.

More at MIT News: http://newsoffice.mit.edu/2015/visual-scenes-object-recognition-0508