Abstract book (pdf) - ICPR 2010

This paper proposes a solution to the problem of aggregating versatile probabilistic models, namely mixtures of probabilistic
principal component analyzers. These models are a powerful generative form for capturing high-dimensional, non
Gaussian, data. They simultaneously perform mixture adjustment and dimensionality reduction. We demonstrate how such
models may be advantageously aggregated by accessing mixture parameters only, rather than original data. Aggregation
is carried out through Bayesian estimation with a specific prior and an original variational scheme. Experimental results
illustrate the effectiveness of the proposal.
15:00-17:10, Paper MoBT9.52
Kernel Uncorrelated Adjacent-Class Discriminant Analysis
Jing, Xiaoyuan, Nanjing Univ. of Posts and Telecommunications
Li, Sheng, Nanjing Univ. of Posts and Telecommunications
Yao, Yongfang, Nanjing Univ. of Posts and Telecommunications
Bian, Lusha, Nanjing Univ. of Posts and Telecommunications
Yang, Jingyu, Nanjing Univ. of Science and Tech.
In this paper, a kernel uncorrelated adjacent-class discriminant analysis (KUADA) approach is proposed for image recognition.
The optimal nonlinear discriminant vector obtained by this approach can differentiate one class and its adjacent
classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices in
kernel space using the Fisher criterion. In this manner, KUADA acquires all discriminant vectors class by class. Furthermore,
KUADA makes every discriminant vector satisfy locally statistical uncorrelated constraints by using the corresponding
class and part of its most adjacent classes. Experimental results on the public AR and CAS-PEAL face databases
demonstrate that the proposed approach outperforms several representative nonlinear discriminant methods.
15:00-17:10, Paper MoBT9.53
A Meta-Learning Approach to Conditional Random Fields using Error-Correcting Output Codes
Ciompi, Francesco, Univ. de Barcelona
Pujol, Oriol, UB
Radeva, Petia, CVC
We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design
of both node potential and edge potential is formulated as a classification problem where margin classifiers are used. The
set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning
problem. The Error-Correcting Output Codes (ECOC) technique is used to deal with the multi-class problem. Furthermore,
the point defined by the combination of margin classifiers in the ECOC space is interpreted in a probabilistic manner, and
the obtained distance values are then converted into potential values. The proposed model exhibits very promising results
when applied to two real detection problems.
15:00-17:10, Paper MoBT9.54
Statistical Modeling of Image Degradation based on Quality Metrics
Chetouani, Aladine, Inst. Galilée – Univ. Paris 13
Beghdadi, Azeddine, Univ. Paris 13
Deriche, Mohamed, KFUPM
A plethora of Image Quality Metrics (IQM) has been proposed during the last two decades. However, at present time,
there is no accepted IQM able to predict the perceptual level of image degradation across different types of visual distortions.
Some measures are more adapted for a set of degradations but inefficient for others. Indeed, the efficiency of any
IQM has been shown to depend upon the type of degradation. Thus, we propose here a new approach for predicting the
type of degradation before using IQMs. The basic idea is first to identify the type of distortion using a Bayesian approach,
then select the most appropriate IQM for estimating image quality for that specific type of distortion. The performance of
the proposed method is evaluated in terms of classification accuracy across different types of degradations.
15:00-17:10, Paper MoBT9.55
Performance Evaluation of Automatic Feature Discovery Focused within Error Clusters
Wang, Sui-Yu, Lehigh Univ.
Baird, Henry, Lehigh Univ.
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