TY - GEN
T1 - Classification and retrieval of ancient watermarks
AU - Brunner, Gerd
AU - Burkhardt, Hans
PY - 2008
Y1 - 2008
N2 - Watermarks in papers have been in use since 1282 in Medieval Europe. Watermarks can be understood much in the sense of being an ancient form of a copyright signature. The interest of the International Association of Paper Historians (IPH) lies specifically in the categorical determination of similar ancient watermark signatures. The highly complex structure of watermarks can be regarded as a strong and discriminative property. Therefore we introduce edge-based features that are incorporated for retrieval and classification. The feature extraction method is capable of representing the global structure of the watermarks, as well as local perceptual groups and their connectivity. The advantage of the method is its invariance against changes in illumination and similarity transformations. The classification results have been obtained with leave-one out tests and a support vector machine (SVM) with an intersection kernel. The best retrieval results have been received with the histogram intersection similarity measure. For the 14 class problem we obtain a true positive rate of more than 87%, that is better than any earlier attempt.
AB - Watermarks in papers have been in use since 1282 in Medieval Europe. Watermarks can be understood much in the sense of being an ancient form of a copyright signature. The interest of the International Association of Paper Historians (IPH) lies specifically in the categorical determination of similar ancient watermark signatures. The highly complex structure of watermarks can be regarded as a strong and discriminative property. Therefore we introduce edge-based features that are incorporated for retrieval and classification. The feature extraction method is capable of representing the global structure of the watermarks, as well as local perceptual groups and their connectivity. The advantage of the method is its invariance against changes in illumination and similarity transformations. The classification results have been obtained with leave-one out tests and a support vector machine (SVM) with an intersection kernel. The best retrieval results have been received with the histogram intersection similarity measure. For the 14 class problem we obtain a true positive rate of more than 87%, that is better than any earlier attempt.
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U2 - 10.1007/978-3-540-78246-9_28
DO - 10.1007/978-3-540-78246-9_28
M3 - Conference contribution
AN - SCOPUS:84879573269
SN - 9783540782391
T3 - Studies in Classification, Data Analysis, and Knowledge Organization
SP - 237
EP - 244
BT - Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007
PB - Kluwer Academic Publishers
T2 - 31st Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Data Analysis, Machine Learning, and Applications, GfKl 2007
Y2 - 7 March 2007 through 9 March 2007
ER -