Image Classification using Statistical Learning Methods


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.

Share and Cite:

J. Mtimet and H. Amiri, "Image Classification using Statistical Learning Methods," Journal of Software Engineering and Applications, Vol. 5 No. 12B, 2012, pp. 200-203. doi: 10.4236/jsea.2012.512B038.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Chih-Fong Tsai, On Classifying Digital Accounting Documents, The International Journal of Digital Ac-counting Research, Vol. 7, N. 13, pp. 53-71, 2007
[2] S.J. Simske, Low-resolution photo/drawing classification: metrics, method and archiving optimiza-tion, Proceedings IEEE ICIP, IEEE, Genoa, Italy, pp. 534-537, 2005.
[3] 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.
[4] M. M. Gorkani and R. W. Picard, Texture orientation for sorting photos ’at a Glance’, Proc. ICPR, pp. 459-464 Oct. 1994
[5] S. Prabhakar, H. Cheng, J.C. Handley, Z. Fan Y.W. Lin, Picture-graphics Color Image Classification, Proc. of ICIP, pp. 785-788, 2002.
[6] R. Schettini, C. Brambilla, G. Ciocca, Val-sasna,M. De Ponti, A hierarchical classification strategy for digital documents, Pattern Recognition, vol 35, pp. 1759-1769, 2002.
[7] Olivier Bousquet, Stéphane Boucheron, and Gabor Lugosi, Introduction to Statistical Learning Theory, Advanced Lectures on Machine Learning, pp.169-207, 2003
[8] S. B. Kotsiantis, Su-pervised Machine Learning: A Review of Classification Techniques, Informatica journal, Volume 31, Number 3, pp. 249-268, 2007.
[9] Jay Gao, Decision Tree Image Analysis, Digital Analysis of Remotely Sensed Imagery book, The McGraw-Hill Companies, Inc. pp.351-388, 2009.
[10] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, New York: Chapman & Hall, 1984.
[11] G.P. Zhang, Neural Network for classification: A Survey, IEEE Transaction on Systems, Man and Cybernetics-Part C: applications and reviews, Vol.30, no. 4, pp. 451-462, 2000.
[12] Ajith Abraham, Artificial Neural Networks, Handbook of Measuring System Design, Peter Syden-ham and Richard Thorn (Eds.), John Wiley and Sons Ltd., London, pp. 901-908, 2005.
[13] 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.
[14] Lamiroy, Bart and Sun, Tao, Precision and Recall Without Ground Truth, In Ninth IAPR International Workshop on Graphics RECognition – GREC 2011, Seoul, Core, sep. 2011.
[15] John Makhoul and Francis Kubala and Richard Schwartz and Ralph Weischedel,Performance Measures For Information Extraction, In Proceedings of DARPA Broadcast News Workshop, pp. 249-252,1999.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.