APPLIED MATH SEMINAR

Title:  Statistical Approaches to Texture Classification
Speaker:  Manik Varma, MSRI
When/where: Friday, April 29th, 2:00pm, Rm 200 AKW
 
Abstract:

In this talk, we address the issue of material classification from
single images obtained under unknown viewpoint and illumination. It is
demonstrated that materials can be classified using the joint
distribution of intensity values over extremely compact neighbourhoods
(starting from as small as 3x3 pixels square), and that this
outperforms classification using filter banks with large support. It
is also shown that the performance of filter banks is inferior to that
of image patches with equivalent neighbourhoods. We develop a novel
texton based representation which is suited to modelling this joint
neighbourhood distribution for MRFs. The representation is learnt from
training images, and then used to classify novel images (with unknown
viewpoint and lighting) into texture classes. The classification
performance surpasses that of recent state of the art filter bank
based classifiers such as Leung and Malik (IJCV 01), Cula and Dana
(IJCV 04), and Varma and Zisserman (IJCV 05).
Joint work with Andrew Zisserman at the University of Oxford