Pattern recognition is a scientific approach for categorizing objects to class or subject numbers. These subjects need to be classified based on their applications (can be image, signal or any other type of measurements). Occupation, automation, military information, communication, industry and commercial applications and many other fields can benefit from Pattern recognition approaches. Perhaps, one the most important reasons that lead to pattern recognition prominent place in today's research studies, is the role of image auto-classifications. In this research, we investigate recent literatures about image auto-classification and image processing to identify patterns.
| Published in |
International Journal of Intelligent Information Systems (Volume 3, Issue 6-1)
This article belongs to the Special Issue Research and Practices in Information Systems and Technologies in Developing Countries |
| DOI | 10.11648/j.ijiis.s.2014030601.25 |
| Page(s) | 80-83 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Pattern Recognition, Images Auto-Classification, Image Processing, Support Vector Machine
| [1] | Milewski, Robert; Govindaraju, Venu (31 March 2008). "Binarization and cleanup of handwritten text from carbon copy medical form images". Pattern Recognition 41 (4): 1308–1315. doi:10.1016/j.patcog.2007.08.018. |
| [2] | Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3 |
| [3] | R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, ISBN 978-0-470-51706-2, 2009. |
| [4] | Quinlan, J. R., (1986). Induction of Decision Trees. Machine Learning 1: 81-106, Kluwer Academic Publishers |
| [5] | Rokach, Lior; Maimon, O. (2008). Data mining with decision trees: theory and applications. World Scientific Pub Co Inc. ISBN 978-9812771711. |
| [6] | Barros R. C., Cerri R., Jaskowiak P. A., Carvalho, A. C. P. L. F., A bottom-up oblique decision tree induction algorithm. Proceedings of the 11th International Conference on Intelligent Systems Design and Applications (ISDA 2011).Conference Location :Cordoba. DOI:10.1109ISDA.2011.6121697 |
| [7] | Cortes, Corinna; and Vapnik, Vladimir N.; "Support-Vector Networks", Machine Learning, 20, 1995. |
| [8] | Aizerman, Mark A.; Braverman, Emmanuel M.; and Rozonoer, Lev I. (1964). "Theoretical foundations of the potential function method in pattern recognition learning". Automation and Remote Control25: 821–837. |
| [9] | Boser, Bernhard E.; Guyon, Isabelle M.; and Vapnik, Vladimir N.; A training algorithm for optimal margin classifiers. In Haussler, David (editor); 5th Annual ACM Workshop on COLT, pages 144–152, Pittsburgh, PA, 1992. ACM Press. doi:10.1145/130385.130401. ISBN 089791497X. |
| [10] | Duan, Kai-Bo; and Keerthi, S. Sathiya (2005). "Which Is the Best Multiclass SVM Method? An Empirical Study". Proceedings of the Sixth International Workshop on Multiple Classifier Systems. Lecture Notes in Computer Science 3541: 278. doi:10.1007/11494683_28. ISBN 978-3-540-26306-7. |
| [11] | Hsu, Chih-Wei; and Lin, Chih-Jen (2002). "A Comparison of Methods for Multiclass Support Vector Machines". Neural Networks, IEEE Transactions on (Volume:13 , Issue: 2 ). DOI:10.1109/72.991427. |
| [12] | Liou, D.-R.; Liou, J.-W.; Liou, C.-Y. (2013). "Learning Behaviors of Perceptron". ISBN 978-1-477554-73-9. iConcept Press. |
APA Style
Mohammad Hadi Yousofi, Habib Yousofi, Sayyed Amir Mohammad Razavi. (2014). Utilizing Automatic Recognition and Classification of Images for Pattern Recognition. International Journal of Intelligent Information Systems, 3(6-1), 80-83. https://doi.org/10.11648/j.ijiis.s.2014030601.25
ACS Style
Mohammad Hadi Yousofi; Habib Yousofi; Sayyed Amir Mohammad Razavi. Utilizing Automatic Recognition and Classification of Images for Pattern Recognition. Int. J. Intell. Inf. Syst. 2014, 3(6-1), 80-83. doi: 10.11648/j.ijiis.s.2014030601.25
AMA Style
Mohammad Hadi Yousofi, Habib Yousofi, Sayyed Amir Mohammad Razavi. Utilizing Automatic Recognition and Classification of Images for Pattern Recognition. Int J Intell Inf Syst. 2014;3(6-1):80-83. doi: 10.11648/j.ijiis.s.2014030601.25
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijiis.s.2014030601.25,
author = {Mohammad Hadi Yousofi and Habib Yousofi and Sayyed Amir Mohammad Razavi},
title = {Utilizing Automatic Recognition and Classification of Images for Pattern Recognition},
journal = {International Journal of Intelligent Information Systems},
volume = {3},
number = {6-1},
pages = {80-83},
doi = {10.11648/j.ijiis.s.2014030601.25},
url = {https://doi.org/10.11648/j.ijiis.s.2014030601.25},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2014030601.25},
abstract = {Pattern recognition is a scientific approach for categorizing objects to class or subject numbers. These subjects need to be classified based on their applications (can be image, signal or any other type of measurements). Occupation, automation, military information, communication, industry and commercial applications and many other fields can benefit from Pattern recognition approaches. Perhaps, one the most important reasons that lead to pattern recognition prominent place in today's research studies, is the role of image auto-classifications. In this research, we investigate recent literatures about image auto-classification and image processing to identify patterns.},
year = {2014}
}
TY - JOUR T1 - Utilizing Automatic Recognition and Classification of Images for Pattern Recognition AU - Mohammad Hadi Yousofi AU - Habib Yousofi AU - Sayyed Amir Mohammad Razavi Y1 - 2014/11/05 PY - 2014 N1 - https://doi.org/10.11648/j.ijiis.s.2014030601.25 DO - 10.11648/j.ijiis.s.2014030601.25 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 80 EP - 83 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.s.2014030601.25 AB - Pattern recognition is a scientific approach for categorizing objects to class or subject numbers. These subjects need to be classified based on their applications (can be image, signal or any other type of measurements). Occupation, automation, military information, communication, industry and commercial applications and many other fields can benefit from Pattern recognition approaches. Perhaps, one the most important reasons that lead to pattern recognition prominent place in today's research studies, is the role of image auto-classifications. In this research, we investigate recent literatures about image auto-classification and image processing to identify patterns. VL - 3 IS - 6-1 ER -
Email: