Bilinear models of natural images

Bruno A. Olshausen; Charles Cadieu; Jack Culpepper; David K Warland

doi:10.1117/12.715515

7 February 2007 Bilinear models of natural images

Bruno A. Olshausen, Charles Cadieu, Jack Culpepper, David K Warland

Proceedings Volume 6492, Human Vision and Electronic Imaging XII; 649206 (2007) https://doi.org/10.1117/12.715515
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

Previous work on unsupervised learning has shown that it is possible to learn Gabor-like feature representations, similar to those employed in the primary visual cortex, from the statistics of natural images. However, such representations are still not readily suited for object recognition or other high-level visual tasks because they can change drastically as the image changes to due object motion, variations in viewpoint, lighting, and other factors. In this paper, we describe how bilinear image models can be used to learn independent representations of the invariances, and their transformations, in natural image sequences. These models provide the foundation for learning higher-order feature representations that could serve as models of higher stages of processing in the cortex, in addition to having practical merit for computer vision tasks.

Citation Download Citation

Bruno A. Olshausen, Charles Cadieu, Jack Culpepper, and David K Warland "Bilinear models of natural images", Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 649206 (7 February 2007); https://doi.org/10.1117/12.715515

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available