Total views : 299

Pattern Recognition using Normalized Feature Vectors Analysis

Affiliations

  • Department of Computer science, Guru Kashi University, Talwandi Sabo – 151302, Punjab, India

Abstract


Objective: To study pattern recognition and retrieval in machine vision system application. Methods/Analysis: Regular and irregular pattern recognition algorithm based on sorting of radii from centre of mass is used and other tools such as Matlab, Neural networks. Findings: The radii from centre of mass to contour of the pattern are computed and sorted in descending order. Few top radii are taken for recognition of the given pattern. As the radii are sorted in descending order, therefore, if the pattern is orientated at any angle, the top order radii are same. This enables the pattern recognition at any orientation. Further, the radii are normalized with respect to their mean radius to make them size invariant. In addition, area, perimeter and euler number are also computed for enhancing the uniqueness degree in features vector set.

Keywords

CBIR, Extraction, Normalization, NN.

Full Text:

 |  (PDF views: 220)

References


  • Arulmozhi V. Classification task by using Matlab Neural Network Tool Box – A Beginner’s View. International Journal of Wisdom Based Computing. 2011 Aug; 1(2):59–61.
  • Ou G, Murphey YL. Multi-class pattern classification using neural networks. Elsevier, Pattern Recognition. 2007 Jan; 40(1):4–18.
  • Mirzaaghazadeh A, Motameni H, Karshenas M, Nematzadeh H. Learning flexible neural networks for pattern recognition. World Academy of Science, Engineering and Technology. 2007; 33:88–92.
  • Jain AK. Duin RPW, Mao J. Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000 Jan; 22(1):4–37.
  • Hassan A, Baksh MSN, Shahroun AM, Jamaluddin H. Improved SPC chart pattern recognition using statistical features. Int J Prod Res. 2003; 41(7):1587–603.
  • Cho SB. Neural-Network Classifiers for recognizing totally unconstrained handwritten numerals. IEEE Transactions on Neural Networks. 1997 Jan; 8(1):43–53.
  • Alsultanny YA, Aqel MM. Pattern recognition using multilayer neural-genetic algorithm. Neurocomputing (51) Elsevier. 2003; 51:237–47.
  • Mureşan RC. Pattern recognition using pulse-coupled neural networks and discrete fourier transforms. Neurocomputing. 2003; 51:487–93.
  • Vehtari A, Lampinen J. Bayesian MLP neural networks for image analysis. Pattern Recognition Letters; 2000 Sep. p. 1–8.
  • Ganesan S, Subashini TS. A content based approach to medical X-Ray image retrieval using texture features. International Journal of Computers and Technology. 2014; 12(7):3742–8.
  • Helala MA, Selim MM, Zayed HH. A content based image retrieval approach based on principal regions detection. IJCSI International Journal of Computer Science Issues. 2012; 9(4):204–13.
  • Qi X, Han Y. A novel fusion approach to content-based image retrieval. The Journal of the Pattern Recognition Society. 2005 Dec; 38(12):2449–65.
  • Liu Y, Zhanga D, Lu G, Ma WY. A survey of content-based image retrieval with high level semantics. The Journal of the Pattern Recognition Society. 2007; 40(1):262–82.
  • Rugna JD, Chareyron G, Konik H. About segmentation step in content-based image retrieval systems. Proceedings of the World Congress on Engineering and Computer Science. 2011; 1.
  • Singh S, Rajavat A. Content based image indexing based on framelet transform and color. International Journal of Scientific and Engineering Research. 2013; 4(9):1490–2.
  • Hiremath PS, Pujari J. Content based image retrieval based on color, texture and shape features using image and its complement. International Journal of Computer Science and Security. 2007; 1(4):25–35.
  • Jayaprabha P, Somasundaram R. Content based image retrieval methods using self supporting retrieval map algorithm. International Journal of Advanced Research in Computer Science and Software Engineering. 2012; 2(1).
  • Soman S, Ghorpade M, Sonone V, Chavan S. Content based image retrieval using advanced color and texture features. Proceedings published in International Journal of Computer Applications (IJCA); 2011. p. 10–4.
  • Rao CS, Kumar SS, Mohan BC. Contents based image retrieval using exact legendre moments and support vector machine. IJMA. 2010; 2(2):69–79.

Refbacks



Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.