PT Unknown
AU Mohammad ali Bagheri
   Qigang Gao
   Sergio Escalera
TI Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers
BT 26th Canadian Conference on Artificial Intelligence
PY 2013
BP 1
EP 12
VL 7884
DI 10.1007/978-3-642-38457-8_1
DE Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature
AB Best paper awardThe performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology.
ER