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4.3 Traffic Sign Classification
The last experiment is a real traffic sign classification
problem. We used the video sequences obtained from
the Mobile Mapping System of (Casacuberta et al.,
2004) for the experiment. The database contains a to-
tal of 2000 samples divided in nine classes. Figure
6 shows several samples of the speed database used
for the experiment. Note the difference in size, illu-
mination, and small affine deformations. We choose
the speed data set since the low resolution of the im-
age, the non-controlled conditions, and the high sim-
ilarity among classes make the categorization a very
difficult task. In this experiment, we used the ECOC-
ONE coding strategy, that showed to outperform the
other coding strategies at the previous experiments, to
test the performance of the decoding strategies. The
classification results are shown in fig. 7 for Discrete
Adaboost and RBF SVM, respectively. The graphic
shows the better performance of the LW variants in
comparison with the rest of the decoding strategies.
In particular, the Exponential LW with the margin of
the classifier attains the higher performance, with an
accuracy over 90%.
Figure 6: Speed traffic sign classes.
Figure 7: Classification results for the speed database. Dis-
crete Adaboost (left bar). RBF SVM (right bar).
5 CONCLUSIONS
In this paper, we presented the Loss-Weighted decod-
ing strategy, that obtains a very high performance ei-
ther in the binary and in the ternary ECOC frame-
work. The Loss-Weighted algorithm shows higher ro-
bustness and better performance than the state-of-the-
art decoding strategies. The validation of the results
is performed using the state-of-the-art coding and de-
coding strategies with Adaboost and SVM as base
classifiers, categorizing a wide set of datasets from the
UCI Machine Learning repository, and dealing with
two real Computer Vision problems.
ACKNOWLEDGEMENTS
This work has been supported in part by the projects
TIN2006-15308-C02-01 and FIS ref. PI061290.
REFERENCES
Allwein, E., Schapire, R., and Singer, Y. (2002). Reducing
multiclass to binary: A unifying approach for margin
classifiers. In JMLR, volume 1, pages 113–141.
Casacuberta, J., Miranda, J., Pla, M., Sanchez, S., A.Serra,
and J.Talaya (2004). On the accuracy and performance
of the geomobil system. In International Society for
Photogrammetry and Remote Sensing.
Dekel, O. and Singer, Y. (2002). Multiclass learning by
probabilistic embeddings. In NIPS, volume 15.
Dietterich, T. and Bakiri, G. (1995). Solving multiclass
learning problems via error-correcting output codes.
In JAIR, volume 2, pages 263–286.
Dietterich, T. and Kong, E. (1995). Error-correcting output
codes corrects bias and variance. In ICML, pages 313–
321.
Escalera, S., Pujol, O., and Radeva, P. (2006). Ecoc-one: A
novel coding and decoding strategy. In ICPR.
Friedman, J., Hastie, T., and Tibshirani, R. (1998). Additive
logistic regression: a statistical view of boosting. In
Technical Report.
http://www.icc.es.
http://www.ics.uci.edu/ mlearn/MLRepository.html.
Ishii, N., Tsuchiya, E., Bao, Y., and Yamaguchi, N. (2005).
Combining classification improvements by ensemble
processing. In Int. proc. in conf. ACIS, pages 240–246.
Karla, C., Joel, B., Oriol, P., Salvatella, N., and Radeva, P.
(2006). In-vivo ivus tissue classification: a compari-
son between rf signal analysis and reconstructed im-
ages. In Progress in Pattern Recognition, pages 137–
146. Springer Berlin / Heidelberg.
OSU-SVM-TOOLBOX. http://svm.sourceforge.net.
Passerini, A., Pontil, M., and Frasconi, P. (2004). New re-
sults on Error Correcting Output Codes of kernel ma-
chines. In IEEE Trans. Neural Networks, volume 15,
pages 45–54.
Pujol, O., Radeva, P., and Vitri`a, J. (2006). Discrimi-
nant ECOC: a heuristic method for application depen-
dent design of error correcting output codes. In IEEE
Trans. of PAMI, volume 28, pages 1007–1012.
Windeatt, T. and Ghaderi, R. (2003). Coding and decod-
ing for multiclass learning problems. In Information
Fusion, volume 1, pages 11–21.
Zhou, J. and Suen, C. (2005). Unconstrained numeral
pair recognition using enhanced error correcting out-
put coding: a holistic approach. In ICDAR, volume 1,
pages 484–488.
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