An Image Analysis Approach to the Calligraphy of Books
Table 2: Classification accuracy and number of used features considering different measurements.
['[EMPTY]', 'EM Accuracy', 'EM Features', 'KMeans Accuracy', 'KMeans Features']
-S3SSx3. These features were selected and ordered according to SVM’s (Support Vector Machine) attribute selection Guyon et al. Furthermore, we used expectation maximization (EM) The number of classes was set to 10 in order to reflect the number of authors. The best classification accuracy, 54%, was achieved using n=14. This result supports the hypothesis that there are visual differences among the visualizations of the mesoscopic networks, which are captured by the image features.