Abstract book (pdf) - ICPR 2010

15:00-17:10, Paper MoBT9.21
Verification under Increasing Dimensionality
Hendrikse, Anne, Univ. of Twente
Veldhuis, Raymond, Univ. of Twente
Spreeuwers, Luuk, Univ. of Twente
Verification decisions are often based on second order statistics estimated from a set of samples. Ongoing growth of computational
resources allows for considering more and more features, increasing the dimensionality of the samples. If the
dimensionality is of the same order as the number of samples used in the estimation or even higher, then the accuracy of
the estimate decreases significantly. In particular, the eigenvalues of the covariance matrix are estimated with a bias and
the estimate of the eigenvectors differ considerably from the real eigenvectors. We show how a classical approach of verification
in high dimensions is severely affected by these problems, and we show how bias correction methods can reduce
these problems.
15:00-17:10, Paper MoBT9.22
Discriminant Feature Manifold for Facial Aging Estimation
Fang, Hui, Swansea Univ.
Grant, Phil, Swansea Univ.
Min, Chen, Swansea Univ.
Computerised facial aging estimation, which has the potential for many applications in human-computer interactions, has
been investigated by many computer vision researchers in recent years. In this paper, a feature-based discriminant subspace
is proposed to extract more discriminating and robust representations for aging estimation. After aligning all the faces by
a piece-wise affine transform, orthogonal locality preserving projection (OLPP) is employed to project local binary patterns
(LBP) from the faces into an age-discriminant subspace. The feature extracted from this manifold is more distinctive for
age estimation compared with the features using in the state-of-the-art methods. Based on the public database FG-NET,
the performance of the proposed feature is evaluated by using two different regression techniques, quadratic function and
neural-network regression. The proposed feature subspace achieves the best performance based on both types of regression.
15:00-17:10, Paper MoBT9.23
Tensor Voting based Color Clustering
Nguyen Dinh, Toan, Chonnam National Univ.
Park, Jonghyun, Chonnam National Univ.
Lee, Chilwoo, Chonnam National Univ.
Lee, Gueesang, Chonnam National Univ.
A novel color clustering algorithm based on tensor voting is proposed. Each color feature vector is encoded by a second
order tensor. Tensor voting is then applied to estimate the number of dominant colors and perform color clustering by exploiting
the shape and data density of the color clusters. The experimental results show that the proposed method generates
good results in image segmentation, especially in the case of images with multi-color texts.
15:00-17:10, Paper MoBT9.24
An Improved Structural EM to Learn Dynamic Bayesian Nets
De Campos, Cassio, Dalle Molle Inst. For Artificial Intelligence
Zeng, Zhi, Rensselaer Pol. Inst.
Ji, Qiang, RPI
This paper addresses the problem of learning structure of Bayesian and Dynamic Bayesian networks from incomplete
data based on the Bayesian Information Criterion. We describe a procedure to map the problem of the dynamic case into
a corresponding augmented Bayesian network through the use of structural constraints. Because the algorithm is exact
and anytime, it is well suitable for a structural Expectation–Maximization (EM) method where the only source of approximation
is due to the EM itself. We show empirically that the use a global maximizer inside the structural EM is computationally
feasible and leads to more accurate models.
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