![](bg6.png)
Also is interesting to observe that with 13
independent components we obtain a very good
result of 80.40% ± 0.78 that outperforms the
previous results showed in (Briceño et al., 2002). As
the number of components is small, we simplify the
classification step.
7 CONCLUSIONS
In this present work, we have presented an
improvement of an automatic leaves recognition
system using Independent Component Analysis and
classifying with multilayer perceptron neural
network. The transformation and reduction of data
contribute to increase its discrimination, from
78.33% using contour parameterization + HMM
(Briceño et al., 2002), to 80.77% using contour
parameterization + ICA + Neural Network.
The advantage of using ICA is twofold: first, we
increase the classification results, specially
diminishing the variance, and second we reduce the
features dimension, giving as a result a less complex
classifier.
Future work will be done exploring other
different ICA algorithms combined with other
classifiers in order to diminish the complexity of the
whole classification system.
ACKNOWLEDGEMENTS
The first author acknowledges support from the
Ministerio de Educación y Ciencia of Spain under
the grant TEC2007-61535/TCM, and from the
Universitat de Vic under the grant R0912.
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