Share Email Print

Proceedings Paper

Image manifolds
Author(s): Haw-minn Lu; Yeshaiahu Fainman; Robert Hecht-Nielsen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

A collection of related N by M images, such as a set of faces, may be modeled by a manifold embedded in an NM- dimensional Euclidean space called an image manifold. With the modeling of image spaces as manifolds, geometrical properties of image manifolds can be studied either theoretically or experimentally. A practical result of the investigation of image manifolds provides an insight into image source entropy (i.e., image compressibility), a subject about which, oddly, little is known. The investigation begins with the most basic properties of a manifold, its dimension and its curvature. The study of dimensionality reveals a high embedding ratio, which gives promise of very high compression rates. The curvature of image manifolds is shown to be large indicating that application of traditional linear transform techniques may not fulfill this promise.

Paper Details

Date Published: 1 April 1998
PDF: 12 pages
Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); doi: 10.1117/12.304659
Show Author Affiliations
Haw-minn Lu, Univ. of California/San Diego (United States)
Yeshaiahu Fainman, Univ. of California/San Diego (United States)
Robert Hecht-Nielsen, Univ. of California/San Diego and HNC Software, Inc. (United States)

Published in SPIE Proceedings Vol. 3307:
Applications of Artificial Neural Networks in Image Processing III
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

© SPIE. Terms of Use
Back to Top