Abstract book (pdf) - ICPR 2010

Feature weighting plays an important role in improving the performance of clustering technique. We propose an automated
feature weighting in fuzzy declustering-based vector quantization (FDVQ), namely AFDVQ algorithm, for enhancing effectiveness
and efficiency in classification. The proposed AFDVQ imposes weights on the modified fuzzy c-means (FCM)
so that it can automatically calculate feature weights based on their degrees of importance rather than treating them equally.
Moreover, the extension of FDVQ and AFDVQ algorithms based on generalized improved fuzzy partitions (GIFP), known
as GIFP-FDVQ and GIFP-AFDVQ respectively, are proposed. The experimental results on real data (original and noisy
data) and modified data (biased and noisy-biased data) have demonstrated that the proposed algorithms outperformed
standard algorithms in classifying clusters especially for biased data.
15:00-17:10, Paper MoBT9.47
A Discriminative and Heteroscedastic Linear Feature Transformation for Multiclass Classification
Lee, Hung-Shin, National Taiwan Univ.
Wang, Hsin-Min, Acad. Sinica
Chen, Berlin, National Taiwan Normal Univ.
This paper presents a novel discriminative feature transformation, named full-rank generalized likelihood ratio discriminant
analysis (fGLRDA), on the grounds of the likelihood ratio test (LRT). fGLRDA attempts to seek a feature space, which is
linearly isomorphic to the original n-dimensional feature space and is characterized by a full-rank transformation matrix,
under the assumption that all the class-discrimination information resides in a d-dimensional subspace, through making
the most confusing situation, described by the null hypothesis, as unlikely as possible to happen without the homoscedastic
assumption on class distributions. Our experimental results demonstrate that fGLRDA can yield moderate performance
improvements over other existing methods, such as linear discriminant analysis (LDA) for the speaker identification task.
15:00-17:10, Paper MoBT9.48
Sparse Representation Classifier Steered Discriminative Projection
Yang, Jian, Nanjing Univ. of Science and Tech.
Chu, Delin, National Univ. of Singapore
The sparse representation-based classifier (SRC) has been developed and shows great potential for pattern classification.
This paper aims to gain a discriminative projection such that SRC achieves the optimum performance in the projected
pattern space. We use the decision rule of SRC to steer the design of a dimensionality reduction method, which is coined
the sparse representation classifier steered discriminative projection (SRC-DP). SRC-DP matches SRC optimally in theory.
Experiments are done on the AR and extended Yale B face image databases, and results show the proposed method is
more effective than other dimensionality reduction methods with respect to the sparse representation-based classifier.
15:00-17:10, Paper MoBT9.49
Designing a Pattern Stabilization Method using Scleral Blood Vessels for Laser Eye Surgery
Kaya, Aydin, Hacettepe Univ.
Can, Ahmet Burak, Hacettepe Univ.
Çakmak, Hasan Basri, Ataturk Research Hospital
In laser eye surgery, the accuracy of operation depends on coherent eye tracking and registration techniques. Main approach
used in image processing based eye trackers is extraction and tracking of pupil and limbus regions. In eye registration
step, iris region features extracted from infrared images are used generally. Registration step determines the angular shift
of eye origin by comparing the eye position on operation table with the eye topology obtained before the operation. Registration
is only applied at the beginning but patients movements don not stop during operation. Hence we presented a
method for pattern stabilization which can be repeated during operation at regular intervals. We use scleral blood vessels
as features due to texturedness and resistance to errors caused by pupil center shift and ablation of cornea region.
15:00-17:10, Paper MoBT9.51
Aggregation of Probabilistic PCA Mixtures with a Variational-Bayes Technique over Parameters
Bruneau, Pierrick, Nantes Univ.
Gelgon, Marc, Nantes Univ.
Picarougne, Fabien, Nantes Univ.
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