Generative and Discriminative Image Models

author: John Winn, Microsoft Research, Cambridge, Microsoft Research
published: March 26, 2010,   recorded: December 2009,   views: 398
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

Creating a good probabilistic model for images is a challenging task, due to the large variability in natural images. For general photographs, an ideal generative model would have to cope with scene layout, occlusion, variability in object appearance, variability in object position and 3D rotation and illumination effects like shading and shadows. The formidable challenges in creating such a model have led many researchers to pursue discriminative models, which instead use image features that are largely invariant to many of these sources of variability. In this talk, I will compare both approaches and describe some strengths and weaknesses of each and suggest some directions in which the best aspects of both can be combined.

See Also:

Download slides icon Download slides: nipsworkshops09_winn_gdim_01.pdf (2.6 MB)

Download slides icon Download slides: nipsworkshops09_winn_gdim_01.ppt (16.4 MB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: