TY - GEN
T1 - Logo recognition based on the Dempster-Shafer fusion of multiple classifiers
AU - Bagheri, Mohammad Ali
AU - Gao, Qigang
AU - Escalera, Sergio
PY - 2013
Y1 - 2013
N2 - The 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.
AB - The 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.
KW - Dempster-Shafer fusion
KW - ensemble classification
KW - generic Fourier descriptor
KW - Logo recognition
KW - shape signature
KW - Zernike moments
UR - http://www.scopus.com/inward/record.url?scp=84884471698&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38457-8_1
DO - 10.1007/978-3-642-38457-8_1
M3 - Article in proceeding
AN - SCOPUS:84884471698
SN - 9783642384561
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 12
BT - Advances in Artificial Intelligence - 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Proceedings
T2 - 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013
Y2 - 28 May 2013 through 31 May 2013
ER -