Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT8.19
Word Clustering using PLSA Enhanced with Long Distance Bigrams
Bassiou, Nikoletta, Aristotle Univ. of Thessaloniki
Kotropoulos, Constantine, Aristotle Univ. of Thessaloniki
Probabilistic latent semantic analysis is enhanced with long distance bigram models in order to improve word clustering.
The long distance bigram probabilities and the interpolated long distance bigram probabilities at varying distances within
a context capture different aspects of contextual information. In addition, the baseline bigram, which incorporates triggerpairs
for various histories, is tested in the same framework. The experimental results collected on publicly available corpora
(CISI, Cran field, Medline, and NPL) demonstrate the superiority of the long distance bigrams over the baseline bigrams
as well as the superiority of the interpolated long distance bigrams against the long distance bigrams and the baseline
bigram with trigger-pairs in yielding more compact clusters containing less outliers.
13:30-16:30, Paper ThBCT8.20
Scene Classification using Local Co-Occurrence Feature in Subspace Obtained by KPCA of Local Blob Visual
Words
Hotta, Kazuhiro, Meijo University
In recent years, scene classification based on local correlation of binarized projection lengths in subspace obtained by
Kernel Principal Component Analysis (KPCA) of visual words was proposed and its effectiveness was shown. However,
local correlation of 2 binary features becomes 1 only when both features are 1. In other cases, local correlation becomes
0. This discarded information. In this paper, all kinds of co-occurrence of 2 binary features are used. This is the first device
of our method. The second device is local Blob visual words. Conventional method made visual words from an orientation
histogram on each grid. However, it is too local information. We use orientation histograms in a local Blob on grid as a
basic feature and develop local Blob visual words. The third device is norm normalization of each orientation histogram
in a local Blob. By normalizing local norm, the similarity between corresponding orientation histogram is reflected in
subspace by KPCA. By these 3 devices, the accuracy is achieved more than 84% which is higher than conventional methods.
13:30-16:30, Paper ThBCT8.21
Recognition and Prediction of Situations in Urban Traffic Scenarios
Käfer, Eugen, Daimler AG
Hermes, Christoph, Bielefeld Univ.
Wöhler, Christian, Dortmund University of Technology
Kummert, Franz, Bielefeld Univ.
Ritter, Helge, Bielefeld Univ.
The recognition and prediction of intersection situations and an accompanying threat assessment are an indispensable skill
of future driver assistance systems. This study focuses on the recognition of situations involving two vehicles at intersections.
For each vehicle, a set of possible future motion trajectories is estimated and rated based on a motion database for
a time interval of 2-4 s ahead. Possible situations involving two vehicles are generated by a pairwise combination of these
individual motion trajectories. An interaction model based on the mutual visibility of the vehicles and the assumption that
a driver will attempt to avoid a collision is used to rate possible situations. The correspondingly favoured situations are
classified with a probabilistic framework. The proposed method is evaluated on a real-world differential GPS data set acquired
during a test drive of about 10 km, including three road intersections. Our method is typically able to recognise the
situation correctly about 1.5-3 s before the last vehicle has passed its minimum distance to the centre of the intersection.
13:30-16:30, Paper ThBCT8.22
Employing Decoding of Specific Error Correcting Codes as a New Classification Criterion in Multiclass Learning
Problems
Luo, Yurong, Virginia Commonwealth Univ.
Kayvan, Najarian, Virginia Commonwealth Univ.
Error Correcting Output Codes (ECOC) method solves multiclass learning problems by combining the outputs of several
binary classifiers according to an error correcting output code matrix. Traditionally, the minimum Hamming distance is
adopted as the classification criterion to “vote” among multiple hypotheses, and the focus is given to the choice of error
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