de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Extracting Structures in Image Collections for Object Recognition

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44365

Ebert,  Sandra
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44887

Larlus,  Diane
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Ebert, S., Larlus, D., & Schiele, B. (2010). Extracting Structures in Image Collections for Object Recognition. In K. Daniilidis, P. Maragos, & N. Paragios (Eds.), Computer Vision - ECCV 2010 (pp. 720-733). Berlin: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-15C3-D
Abstract
Many computer vision methods rely on annotated image databases without taking advantage of the increasing number of unlabeled images available. This paper explores an alternative approach involving unsupervised structure discovery and semi-supervised learning (SSL) in image collections. Focusing on object classes, the first part of the paper contributes with an extensive evaluation of state-of-the-art image representations underlining the decisive influence of the local neighborhood structure, its direct consequences on SSL results, and the importance of developing powerful object representations. In a second part, we propose and explore promising directions to improve results by looking at the local topology between images and feature combination strategies.