Dahyot, Rozenn and Charbonnier, Pierre and Heitz, Fabrice
(2004)
A Bayesian approach to object detection using probabilistic
appearance-based models.
Pattern Analysis and Applications, 7 (3).
pp. 317-332.
ISSN 1433-7541
Abstract
In this paper, we introduce a Bayesian approach, inspired by probabilistic principal component
analysis (PPCA) (Tipping and Bishop in J Royal Stat Soc
Ser B 61(3):611–622, 1999), to detect objects in complex
scenes using appearance-based models. The originality of
the proposed framework is to explicitly take into account
general forms of the underlying distributions, both for
the in-eigenspace distribution and for the observation
model. The approach combines linear data reduction
techniques (to preserve computational efficiency), nonlinear constraints on the in-eigenspace distribution (to
model complex variabilities) and non-linear (robust)
observation models (to cope with clutter, outliers and
occlusions). The resulting statistical representation generalises most existing PCA-based models (Tipping and
Bishop in J Royal Stat Soc Ser B 61(3):611–622, 1999;
Black and Jepson in Int J Comput Vis 26(1):63–84, 1998;
Moghaddam and Pentland in IEEE Trans Pattern Anal
Machine Intell 19(7):696–710, 1997) and leads to the
definition of a new family of non-linear probabilistic
detectors. The performance of the approach is assessed
using receiver operating characteristic (ROC) analysis on
several representative databases, showing a major
improvement in detection performances with respect
to the standard methods that have been the references up
to now.
| Item Type: |
Article
|
| Keywords: |
Eigenspace representation; Probabilistic
PCA; Bayesian approach; Non-Gaussian models;
M-estimators; Half-quadratic algorithms; |
| Academic Unit: |
Faculty of Science and Engineering > Computer Science |
| Item ID: |
15128 |
| Identification Number: |
https://doi.org/10.1007/s10044-004-0230-5 |
| Depositing User: |
Rozenn Dahyot
|
| Date Deposited: |
14 Dec 2021 16:20 |
| Journal or Publication Title: |
Pattern Analysis and Applications |
| Publisher: |
Springer |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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