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S.J. Simske, Low-resolution photo/drawing classification: metrics, method and archiving optimiza-tion, Proceedings IEEE ICIP, IEEE, Genoa, Italy, pp. 534-537, 2005.

has been cited by the following article:

• TITLE: Image Classification using Statistical Learning Methods

  AUTHORS: Jassem Mtimet, Hamid Amiri

  KEYWORDS: Image classification; Decision Tree; Neuronal Network; statistical analysis

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.12B, January 25, 2013

  ABSTRACT: 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.