Pattern recognition is a tough problem for computers, although humans are wired for it. Pattern recognition is becoming increasingly important in the age of automation and information handling and retrieval. This paper reviews possible application areas of Pattern recognition. Author covers various sub-disciplines of pattern recognition based on learning methods, such as supervised, unsupervised, semi-supervised learning and key research areas such as grammar induction. Novel solutions to these possible problems could be well deployed for character recognition, speech analysis, man and machine diagnostics, person identification, industrial inspection and so on. The paper concludes with brief discussion on open issues that need to be addressed by future researchers.

 

Published In : IJCSN Journal Volume 3, Issue 5

Date of Publication : October 2014

Pages : 326 - 333

Figures : 01

Tables : --

Publication Link : Pattern Recognition: Possible Research Areas and Issues

 

 

 

Jayashree Rajesh Prasad : graduated in Computer Science and Engineering from North Maharashtra University in 1996 and completed M.E. in Computer Engineering from Pune University in 2004. She pursued Ph.D. in Computer Science and engineering from Swami Ramananda Tirtha University, Nanded in 2014. She has a research project “Conversion of Gujrati Script to Speech”, funded by BCUD (University of Pune) to her credit. She works with Sinhgad College of Engineering, Pune. Her research interests are in the field of Soft Computing, pattern recognition and image processing. She is Life member of Computer Society of India, Life Member of Indian Society for Technical Education, Member of IAENG (International Association of Engineers) and Member of IACSIT (International Association of Computer Science and Information Technology).

 

 

 

 

 

 

 

Anomaly detection

Classification

Clustering

Dimensionality reduction

Grammar induction

Feature learning

Supervised

unsupervised learning

The area of pattern recognition has developed itself into a mature engineering field with many practical applications. This increased applicability, together with the development of sensors and computer resources, leads to new research areas and rises new questions. The domain analysis of this field and the relevant literature review finds many old (commonly observed) and new (emerging) open issues that need to be addressed by future researchers in the field. Some of the problems analyzed below may be solved either by a better understanding of their causes or by novel and better procedures [15]. Many emerging applications of pattern recognition involve complicated high-dimensional pattern spaces, small amounts of data-per-dimension, low signal-to-noise ratio, poorly specified statistical distributions and anomalous statistical outliers.

 

 

 

 

 

 

 

 

 

[1] S. B. Kotsiantis · I. D. Zaharakis · P. E. Pintelas, Machine learning: a review of classification and combining techniques, Artif Intell Rev DOI 10.1007/s10462-007-9052-3, © Springer Science Business Media B.V. , 2007 [2] Yujing Zeng, Janusz Starzyk, Statistical Approach to Clustering in Pattern Recognition, 0-7803-6661- 1/01/$10.00, IEEE, 2001. [3] Imran Naseem, Linear Regression for Face Recognition, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 32, NO. 11, NOVEMBER 2010. [4] Vijay Mahadevan Weixin Li Viral Bhalodia Nuno Vasconcelos, Anomaly Detection in Crowded Scenes, IEEE Conf. on Computer Vision and Pattern Recognition, San Francisco, 2010. [5] Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard, QuantMiner for Mining Quantitative Association Rules, Journal of Machine Learning Research 14 (2013) 3153-3157, October 2013. [6] Jing Peng and Bir Bhanu, Closed-Loop Object Recognition Using Reinforcement Learning, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 2, FEBRUARY 1998. [7] Noah A. Smith, Linguistic Structure Prediction, SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES, Morgan & Claypool publishers. [8] Kai Labusch, Udo Siewert, Thomas Martinetz, and Erhardt Barth, Learning optimal features for visual pattern recognition, Institute for Neuro- and Bioinformatics, University of L¨ubeck, Ratzeburger Allee 160, D-23538 L¨ubeck, Germany. [9] V. Jothi Prakash, Dr. L.M. Nithya, A Survey On Semi- Supervised Learning Techniques, International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 1– Feb 2014. [10] Arianna D’Ulizia · Fernando Ferri · Patrizia Grifoni, A survey of grammatical inference methods for natural language learning, Artif Intell Rev DOI 10.1007/s10462-010-9199-1, Springer, January 2011. [11] S. B. Kotsiantis, Supervised Machine Learning: A Review of Classification Techniques, Informatica 31 (2007) 249-268, July 2007. [12] Rui Xu, Student Member, IEEE and Donald Wunsch, Survey of Clustering Algorithms, IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 16, NO. 3, MAY 2005. [13] Yujing Zeng, Janusz Starzyk, Statistical Approach to Clustering in Pattern Recognition, 0-7803-6661- 1/01/$10.00, IEEE, 2001. [14] Imola K. Fodor, A survey of dimension reduction techniques, Center for Applied Scienti¯ c Computing, Lawrence Livermore National Laboratory, 2002. [15] Robert P.W. Duin and El?zbieta P, ekalska1, Open issues in pattern recognition, http://homepage.tudelft.nl/a9p19/papers/cores_05_o pen_issues.pdf [16] Jayashree Rajesh Prasad and Uday Kulkarni, Gujarati Character Recognition using Adaptive Neuro Fuzzy Classifier with Fuzzy Hedges, International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, Springer, DOI 10.1007/s13042-014-0259-8, May 2014. [17] Jayashree Rajesh Prasad and Uday Kulkarni, Gujrati character recognition using weighted k-NN and mean distance measure, International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, Springer, DOI 10.1007/s13042- 013-0187-z, August 2013. [18] Jayashree Rajesh Prasad and Uday Kulkarni, Gujrati Character Recognition using Adaptive Neuro Fuzzy Classifier, In proceedings of IEEE ICESC 2014, DOI 10.1109/ICESC.2014.79, INSPEC Accession Number:14127431, Page(s):402 – 407, January 2014. [19] Jayashree Rajesh Prasad and Uday Kulkarni, Statistical Feature Extraction and Recognition of Isolated Handwritten Gujrati Characters’, CiiT International Journal of Digital Image Processing, Vol. 3, No. 19, December 2011. [20] J. R. Prasad and U. V. Kulkarni, Trends in Handwriting recognition, Proceedings of IEEE ICETET 2010, Goa and IEEE Xplore, ISSN 2157- 0477 E ISBN: 978-0-7695-4246-1 Print ISBN 978- 1-4244-8481-2 INSPEC Accession Number 11832797, DOI 10.1109/ICETET.2010.92, November 2010.