Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
in Proc. 11th IEEE International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil, October 2007 Learning the Taxonomy and Models of Categories Present in Arbitrary Images
 

Summary: in Proc. 11th IEEE International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil, October 2007
Learning the Taxonomy and Models of Categories Present in Arbitrary Images
Narendra Ahuja and Sinisa Todorovic
Beckman Institute, University of Illinois at Urbana-Champaign
{ahuja, sintod}@vision.ai.uiuc.edu
Abstract
This paper proposes, and presents a solution to, the
problem of simultaneous learning of multiple visual cate-
gories present in an arbitrary image set and their inter-
category relationships. These relationships, also called
their taxonomy, allow categories to be defined recursively,
as spatial configurations of (simpler) subcategories each of
which may be shared by many categories. Each image is
represented by a segmentation tree, whose structure cap-
tures recursive embedding of image regions in a multiscale
segmentation, and whose nodes contain the associated re-
gion properties. The presence of any occurring categories
is reflected in the occurrence of associated, similar subtrees
within the image trees. Similar subtrees across the entire
image set are clustered. Each cluster corresponds to a dis-

  

Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences; Engineering