Learning to traverse image manifolds
Research output: Contribution to journal › Conference article › Research › peer-review
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Learning to traverse image manifolds. / Dollár, Piotr; Rabaud, Vincent; Belongie, Serge.
In: Advances in Neural Information Processing Systems, 2007, p. 361-368.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Learning to traverse image manifolds
AU - Dollár, Piotr
AU - Rabaud, Vincent
AU - Belongie, Serge
PY - 2007
Y1 - 2007
N2 - We present a new algorithm, Locally Smooth Manifold Learning (LSML), that learns a warping function from a point on an manifold to its neighbors. Important characteristics of LSML include the ability to recover the structure of the manifold in sparsely populated regions and beyond the support of the provided data. Applications of our proposed technique include embedding with a natural out-of-sample extension and tasks such as tangent distance estimation, frame rate up-conversion, video compression and motion transfer.
AB - We present a new algorithm, Locally Smooth Manifold Learning (LSML), that learns a warping function from a point on an manifold to its neighbors. Important characteristics of LSML include the ability to recover the structure of the manifold in sparsely populated regions and beyond the support of the provided data. Applications of our proposed technique include embedding with a natural out-of-sample extension and tasks such as tangent distance estimation, frame rate up-conversion, video compression and motion transfer.
UR - http://www.scopus.com/inward/record.url?scp=67349259478&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:67349259478
SP - 361
EP - 368
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
SN - 1049-5258
T2 - 20th Annual Conference on Neural Information Processing Systems, NIPS 2006
Y2 - 4 December 2006 through 7 December 2006
ER -
ID: 302051790