In general, digital images can be classified into photographs, textual and mixed documents. This taxonomy is very useful in many applications, such as archiving task. However, there are no effective methods to perform this classification automatically. In this paper, we present a method for classifying and archiving document into the following semantic classes: photographs, textual and mixed documents. Our method is based on combining low-level image features, such as mean, Standard deviation, Skewness. Both the Decision Tree and Neuronal Network Classifiers are used for classification task.
Vailaya, A., Figueiredo, M., A. Jain, and H. J. Zhang, Bayesian framework for hierarchical semantic classification of vacation images, Proceedings of the IEEE International Conference on Multimedia Computing and Systems (ICMSC), pp. 518- 523, Flo-rence, Italy, 1999.
Hyontai Sug, Performance Comparison of RBF networks and MLPs for Classification, Proceedings of the 9th WSEAS Inter-national Conference on applied Informatics and Com-munications (AIC ’09), pp.450-454, 2009.