Abstract book (pdf) - ICPR 2010

elations, whereas neighboring data points of different classes no longer stick to one another. But, neighboring data points
of different classes are not deemphasized efficiently by LDE and it may degrade the performance of classification. In this
paper, we investigated its extension, called class mean embedding (CME), using class mean of data points to enhance its
discriminant power in their mapping into a low dimensional space. Experimental results on ORL and FERET face databases
show the effectiveness of the proposed method.
13:30-16:30, Paper ThBCT8.6
Forest Species Recognition using Color-Based Features
Paula, Pedro Luiz, UFPR
Oliveira, Luiz, Federal Univ. of Parana
Britto, Alceu, Pontificia Univ. Católica do Paraná
Sabourin, R., École de Tech. supérieure
In this work we address the problem of forest species recognition which is a very challenging task and has several potential
applications in the wood industry. The first contribution of this work is a database composed of 22 different species of the
Brazilian flora that has been carefully labeled by expert in wood anatomy. In addition, in this work we demonstrate through
a series of comprehensive experiments that color-based features are quite useful to increase the discrimination power for
this kind of application. Last but not least, we propose a segmentation approach so that a wood can be locally processed
to mitigate the intra-class variability featured in some classes. Such an approach also brings important contribution to improve
the final performance in terms of classification.
13:30-16:30, Paper ThBCT8.7
An Information Theoretic Linear Discriminant Analysis Method
Zhang, Haihong, Inst. for Infocomm Res.
Guan, Cuntai, Inst. for Infocomm Res.
We propose a novel linear discriminant analysis method and demonstrate its superiority over existing linear methods.
Based on information theory, we introduce a non-parametric estimate of mutual information with variable kernel bandwidth.
Furthermore, we derive a gradient-based optimization algorithm for learning the optimal linear reduction vectors which
maximizes the mutual information estimate. We evaluate the proposed method by running cross-validation on 2 data sets
from the UCI repository, together with linear and nonlinear SVMs as classifiers. The result attests to the superority of the
method over conventional LDA and its variant, aPAC.
13:30-16:30, Paper ThBCT8.8
Framewise Phone Classification using Weighted Fuzzy Classification Rules
Dehzangi, Omid, Nanyang Tech. Univ.
Ma, Bin, Inst. for Infocomm Res.
Chng, Eng Siong, Nanyang Tech. Univ.
Li, Haizhou, Inst. for Infocomm Res.
Our aim in this paper is to propose a rule-weight learning algorithm in fuzzy rule-based classifiers. The proposed algorithm
is presented in two modes: first, all training examples are assumed to be equally important and the algorithm attempts to
minimize the error-rate of the classifier on the training data by adjusting the weight of each fuzzy rule in the rule-base,
and second, a weight is assigned to each training example as the cost of misclassification of it using the class distribution
of its neighbors. Then, instead of minimizing the error-rate, the learning algorithm is modified to minimize the sum of
costs for misclassified examples. Using six data sets from UCI-ML repository and the TIMIT speech corpus for frame
wise phone classification, we show that our proposed algorithm considerably improves the prediction ability of the classifier.
13:30-16:30, Paper ThBCT8.9
Statistical Fourier Descriptors for Defect Image Classification
Timm, Fabian, Univ. of Lübeck
Martinetz, Thomas, Univ. of Lübeck
In many industrial applications, Fourier descriptors are commonly used when the description of the object shape is an im-
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