Abstract book (pdf) - ICPR 2010

with Shape from Shading, we fully recover the surface shape. We demonstrate the effectiveness of the robust estimator
compared to the linear least-squares estimator through shape recovery experiments on both synthetic and real images.
TuAT4 Dolmabahçe Hall A
Signal Separation and Classification Regular Session
Session chair: Erzin, Engin (Koc Univ.)
09:00-09:20, Paper TuAT4.1
Classifying Three-Way Seismic Volcanic Data by Dissimilarity Representation
Porro, Diana, Advanced Tech. Application Center
Duin, Robert, TU Delft
Orozco-Alzate, Mauricio, Univ. Nacional de Colombia Sede Manizales
Talavera, Isneri, Advanced Tech. Application Center
Londoño-Bonilla, John Makario, Inst. Colombiano de Geología y Minería
Multi-way data analysis is a multivariate data analysis technique having a wide application in some fields. Nevertheless,
the development of classification tools for this type of representation is incipient yet. In this paper we study the dissimilarity
representation for the classification of three-way data, as dissimilarities allow the representation of multi-dimensional objects
in a natural way. As an example, the classification of seismic volcanic events is used. It is shown that in this application
classification based on 2D spectrograms, dissimilarities perform better than on 1D spectral features.
09:20-09:40, Paper TuAT4.2
Improved Blur Insensitivity for Decorrelated Local Phase Quantization
Heikkilä, Janne, Univ. of Oulu
Ojansivu, Ville, Univ. of Oulu
Rahtu, Esa, Univ. of Oulu
This paper presents a novel blur tolerant I relation scheme for local phase quantization (LPQ) texture descriptor. As opposed
to previous methods, the introduced model can be applied with virtually any kind of blur regardless of the point spread
function. The new technique takes also into account the changes in the image characteristics originating from the blur
itself. The implementation does not suffer from multiple solutions like the I relation in original LPQ, but still retains the
same run-time computational complexity. The texture classification experiments illustrate considerable improvements in
the performance of LPQ descriptors in the case of blurred images and show only negligible loss of accuracy with sharp
images.
09:40-10:00, Paper TuAT4.3
Ensemble Discriminant Sparse Projections Applied to Music Genre Classification
Kotropoulos, Constantine, Aristotle Univ. of Thessaloniki
Arce, Gonzalo, Univ. of Delaware
Panagakis, Yannis, Aristotle Univ. of Thessaloniki
Resorting to the rich, psycho-physiologically grounded, properties of the slow temporal modulations of music recordings,
a novel classifier ensemble is built, which applies discriminant sparse projections. More specifically, over complete dictionaries
are learned and sparse coefficient vectors are extracted to optimally approximate the slow temporal modulations
of the training music recordings. The sparse coefficient vectors are then projected to the principal subspaces of their withinclass
and between-class covariance matrices. Decisions are taken with respect to the minimum Euclidean distance from
the class mean sparse coefficient vectors, which undergo the aforementioned projections. The application of majority
voting to the decisions taken by 10 individual classifiers, which are trained on the 10 training folds defined by stratified
10-fold cross-validation on the GTZAN dataset, yields a music genre classification accuracy of 84.96% on average. The
latter exceeds by 2.46% the highest accuracy previously reported without employing any sparse representations.
- 75 -