Abstract book (pdf) - ICPR 2010

Tefas, Anastasios, Aristotle Univ. of Thessaloniki
Error-Correcting Output Codes (ECOC) with sub-classes reveal a common way to solve multi-class classification problems.
According to this approach, a multi-class problem is decomposed into several binary ones based on the maximization of
the mutual information (MI) between the classes and their respective labels. The MI is modelled through the fast quadratic
mutual information (FQMI) procedure. However, FQMI is not applicable on large datasets due to its high algorithmic
complexity. In this paper we propose Fisher’s Linear Discriminant Ratio (FLDR) as an alternative decomposition criterion
which is of much less computational complexity and achieves in most experiments conducted better classification performance.
Furthermore, we compare FLDR against FQMI for facial expression recognition over the Cohn-Kanade database.
13:30-16:30, Paper WeBCT8.20
Pattern Recognition Method using Ensembles of Regularities Found by Optimal Partitioning
Senko, Oleg, Inst. of Russian Acad. of Sciences
Kuznetsova, Anna, Inst. of Russian Acad. of Sciences
New pattern recognition method is considered that is based on ensembles of syndromes. The developed method that is referred
to as Multi-model statistically weighted syndromes (MSWS) is further development of earlier Statistically Weighted
Syndromes (SWS) method. Syndromes are subregions in space of prognostic features where content of objects from one
of the classes differs significantly from the same class contents in neighboring subregions. Syndromes are discussed as
simple basic classifiers that are combined with the help of weighted voting procedure. Method of optimal partitioning of
input features space is used for syndromes searching. At that syndromes are selected depending on quality of data separation
and complexity of used partitioning model (partitions family). Performance of MSWS is compered with performance of
SWS and alternative techniques in several applied tasks. Influence of recognition ability on characteristics of syndromes
selection is studied.
13:30-16:30, Paper WeBCT8.21
A Geometric Radial Basis Function Network for Robot Perception and Action
Bayro Corrochano, Eduardo Jose, CINVESTAV, Unidad Guadalajara
Vázquez Santacruz, Eduardo, CINVESTAV, Unidad Guadalajara
This paper presents a new hyper complex valued Radial Basis Network. This network constitutes a generalization of the
standard real valued RBF. This geometric RBF can be used in real time to estimate changes in linear transformations between
sets of geometric entities. Experiments using stereo image sequences validate this proposal. We propose a Geometric
RBF Network (GRBF-N) designed in the geometric algebra framework. We present an application to estimate linear transformations
between sets of geometric entities. Our experiments validate our proposal.
13:30-16:30, Paper WeBCT8.22
Kernel on Graphs based on Dictionary of Paths for Image Retrieval
Haugeard, Jean-Emmanuel, ETIS, CNRS, ENSEA, Univ. Cergy-Pontoise
Philipp-Foliguet, Sylvie, ENSEA/UCP/CNRS
Gosselin, Philippe Henri, CNRS
Recent approaches of graph comparison consider graphs as sets of paths. Kernels on graphs are then computed from
kernels on paths. A common strategy for graph retrieval is to perform pairwise comparisons. In this paper, we propose to
follow a different strategy, where we collect a set of paths into a dictionary, and then project each graph to this dictionary.
Then, graphs can be classified using powerful classification methods, such as SVM. Furthermore, we collect the paths
through interaction with a user. This strategy is ten times faster than a straight comparisons of paths. Experiments have
been carried out on a database of city windows.
13:30-16:30, Paper WeBCT8.23
An Efficient Active Constraint Selection Algorithm for Clustering
Vu, Viet-Vu, Univ. Pierre et Marie Curie - Paris 6
Labroche, Nicolas, Univ. Pierre et Marie Curie - Paris 6
Bouchon-Meunier, Bernadette, Univ. Pierre et Marie Curie - Paris 6
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