@inproceedings{d53a1bb1edae420fb7691f28a2070c65,
title = "Object recognition by scene alignment",
abstract = "Current object recognition systems can only recognize a limited number of object categories; scaling up to many categories is the next challenge. We seek to build a system to recognize and localize many different object categories in complex scenes. We achieve this through a simple approach: by matching the input image, in an appropriate representation, to images in a large training set of labeled images. Due to regularities in object identities across similar scenes, the retrieved matches provide hypotheses for object identities and locations. We build a probabilistic model to transfer the labels from the retrieval set to the input image. We demonstrate the effectiveness of this approach and study algorithm component contributions using held-out test sets from the LabelMe database.",
author = "Russell, {Bryan C.} and Antonio Torralba and Ce Liu and Rob Fergus and Freeman, {William T.}",
year = "2008",
language = "English (US)",
isbn = "160560352X",
series = "Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference",
publisher = "Neural Information Processing Systems",
booktitle = "Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference",
note = "21st Annual Conference on Neural Information Processing Systems, NIPS 2007 ; Conference date: 03-12-2007 Through 06-12-2007",
}