Probabilistic visual learning for object representation
Moghaddam, B.
Pentland, A.
Media Lab., MIT, Cambridge, MA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jul 1997
Volume: 19,
Issue: 7
On page(s): 696-710
ISSN: 0162-8828
References Cited: 38
CODEN: ITPIDJ
INSPEC Accession Number: 5661534
Digital Object Identifier: 10.1109/34.598227
Current Version Published: 2002-08-06
Abstract
We present an unsupervised technique for visual learning, which is
based on density estimation in high-dimensional spaces using an
eigenspace decomposition. Two types of density estimates are derived for
modeling the training data: a multivariate Gaussian (for unimodal
distributions) and a mixture-of-Gaussians model (for multimodal
distributions). Those probability densities are then used to formulate a
maximum-likelihood estimation framework for visual search and target
detection for automatic object recognition and coding. Our learning
technique is applied to the probabilistic visual modeling, detection,
recognition, and coding of human faces and nonrigid objects, such as
hands
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