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[nl-uiuc] Upcoming talk at the AIIS seminar (next Thursday).


Chronological Thread 
  • From: "Alexandre Klementiev" <klementi AT uiuc.edu>
  • To: nl-uiuc AT cs.uiuc.edu, aivr AT cs.uiuc.edu, dais AT cs.uiuc.edu, cogcomp AT cs.uiuc.edu, vision AT cs.uiuc.edu, krr-group AT cs.uiuc.edu, group AT vision2.ai.uiuc.edu
  • Subject: [nl-uiuc] Upcoming talk at the AIIS seminar (next Thursday).
  • Date: Fri, 12 Sep 2008 14:26:03 -0500
  • List-archive: <http://lists.cs.uiuc.edu/pipermail/nl-uiuc>
  • List-id: Natural language research announcements <nl-uiuc.cs.uiuc.edu>

Dear faculty and students,

Ronen Basri will give a talk at the AIIS seminar next Thursday (details below)

Hope to see you there
,
Alex.


Title: Approximate Nearest Subspace Search with Applications to Pattern Recognition
Speaker:
Ronen Basri, The Weizmann Institute of Science and TTI Chicago

Date: September 18, 4:00pm
Location: Siebel 3405 


Abstract: 

Linear and affine subspaces are commonly used to describe the appearance of objects under different lighting, viewpoint, articulation, and even identity. A natural problem arising from their use is -- given a query image portion represented as a point in some high dimensional space -- find a subspace near to the query. This talk presents an efficient solution to the approximate nearest subspace problem for both linear and affine subspaces. Our method is based on a simple reduction to the problem of nearest point search, and can thus employ tree based search or locality sensitive hashing to find a near subspace. Further speedup is achieved by using random projections to lower the dimensionality of the problem. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its capabilities on synthetic and real data. Our experiments demonstrate that an approximate nearest subspace can be located significantly faster than the exact nearest subspace, while at the same time it can find better matches compared to a similar search on points, in the presence of variations due to viewpoint, lighting etc.

Joint work with Tal Hassner and Lihi Zelnik-Manor.



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