Skip to Main content Skip to Navigation
New interface
Reports (Technical report)

Project/Team LEAR : Learning and Recognition in Vision

Frédéric Jurie 1 Cordelia Schmid 1, * Bill Triggs 1 
* Corresponding author
1 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : LEAR's main focus of research is learning based approaches to visual object recognition and scene interpretation, particularly for image retrieval and video indexing. Understanding the content of everyday images and videos is one of the most challenging problems in computer vision. The extent to which we can do this is currently limited, but we believe that very significant advances will be made over the next few years by combining emerging statistical learning techniques with state of the art image descriptors. This field is also close to a major threshold of applicability: even partial solutions are likely to enable many new applications.
Document type :
Reports (Technical report)
Complete list of metadata

Cited literature [31 references]  Display  Hide  Download
Contributor : THOTH Team Connect in order to contact the contributor
Submitted on : Monday, December 20, 2010 - 9:09:15 AM
Last modification on : Wednesday, October 26, 2022 - 8:13:58 AM
Long-term archiving on: : Monday, March 21, 2011 - 3:10:40 AM


Files produced by the author(s)


  • HAL Id : inria-00548531, version 1



Frédéric Jurie, Cordelia Schmid, Bill Triggs. Project/Team LEAR : Learning and Recognition in Vision. [Technical Report] 2004, pp.45. ⟨inria-00548531⟩



Record views


Files downloads