Probabilistic Visual Learning for Object Representation

Authors:
Baback Moghaddam;Alex Pentland
Affiliations:
-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1997

Citing 6
Cited 293

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CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
Face for ambient interface

Ambient Intelligence in Everyday Life
An effective method for detecting facial features and face in human-robot interaction

Information Sciences: an International Journal
FaceMouse: a human-computer interface for tetraplegic people

ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
Head gesture recognition based on bayesian network

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
An improved hybrid approach to face recognition by fusing local and global discriminant features

International Journal of Biometrics
Real-time face tracking and recognition by sparse eigentracker with associative mapping to 3D shape

Image and Vision Computing
Incremental threshold learning for classifier selection

Neurocomputing
Discriminant phase component for face recognition

Journal of Electrical and Computer Engineering
Text-image interaction for image retrieval and semi-automatic indexing

IRSG'98 Proceedings of the 20th Annual BCS-IRSG conference on Information Retrieval Research
Survey: Subspace methods for face recognition

Computer Science Review
A rough neurocomputing approach for illumination invariant face recognition system

RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Multi-eigenspace learning for video-based face recognition

ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Online spatio-temporal structural context learning for visual tracking

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Symmetry-driven accumulation of local features for human characterization and re-identification

Computer Vision and Image Understanding
Multi-pose 3D face recognition based on 2D sparse representation

Journal of Visual Communication and Image Representation
Exploiting fisher and fukunaga-koontz transforms in chernoff dimensionality reduction

ACM Transactions on Knowledge Discovery from Data (TKDD)
A literature survey on robust and efficient eye localization in real-life scenarios

Pattern Recognition
Pose invariant face recognition using biological inspired features based on ensemble of classifiers

Journal of Visual Communication and Image Representation
Bayesian 3D shape from silhouettes

Digital Signal Processing
Co-trained generative and discriminative trackers with cascade particle filter

Computer Vision and Image Understanding
Visualization of graphical data in a user-specified 2D space using a weighted Isomap method

Graphical Models

Quantified Score

Hi-index	0.15

Visualization

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). These 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.