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Object Recognition by Scene Alignment
Bryan C Russell · Antonio Torralba · Ce Liu · Rob Fergus · William Freeman

Wed Dec 05 03:20 PM -- 03:30 PM (PST) @ None

Current object recognition systems can only recognize a limited number of object categories; scaling up to many categories is the next challenge in object recognition. We seek to build a system to recognize and localize many different object categories in complex scenes. We achieve this through a deceptively simple approach: by matching the input image, in an appropriate representation, to images in a large training set of labeled images (LabelMe). This provides us with a set of retrieval images, providing hypotheses for object identities and locations. We then transfer the labelings from the retrieval set. We demonstrate the effectiveness of this approach and study algorithm component contributions using held-out test sets from the LabelMe database.

Author Information

Bryan C Russell (U Washington)
Antonio Torralba (Massachusetts Institute of Technology)
Ce Liu (Microsoft Research)
Rob Fergus (DeepMind / NYU)

Rob Fergus is an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University. He received a Masters in Electrical Engineering with Prof. Pietro Perona at Caltech, before completing a PhD with Prof. Andrew Zisserman at the University of Oxford in 2005. Before coming to NYU, he spent two years as a post-doc in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. William Freeman. He has received several awards including a CVPR best paper prize, a Sloan Fellowship & NSF Career award and the IEEE Longuet-Higgins prize.

William Freeman (Massachusetts Institute of Technology)

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