Research Article Open Access

Features Extraction Based on Linear Regression Technique

Khalid W. Magld1
  • 1 ,
Journal of Computer Science
Volume 8 No. 5, 2012, 701-704

DOI: https://doi.org/10.3844/jcssp.2012.701.704

Submitted On: 17 October 2011 Published On: 25 February 2012

How to Cite: Magld, K. W. (2012). Features Extraction Based on Linear Regression Technique. Journal of Computer Science, 8(5), 701-704. https://doi.org/10.3844/jcssp.2012.701.704

Abstract

Problem statement: The matching problem of complex objects is one of the most difficult task in the pattern recognition field. These problems are made difficult by seemingly infinite varieties of shapes and classes which are used. The difficulties are related to absolute shape measurement, given the impossibility of directly mapping shapes, as such, into a feature space. Approach: In this study, an object was modeled using boundaries pixel distance. The invariant has been resulted from the distance of each boundaries pixel to their central point. By performing linear regression on each set of sorted distances, a unique set of numerical features from the coefficients of this linear function has been produced. This unique set of numerical values is then proposed as an object’s features. Results: The experiments show that the coefficient of linear function from boundaries’ distance plot of each object has produced better recognition than polynomial function of degree more than one. Conclusion/Recommendations: More than 200 hundreds trademark’s images have been tested and almost 90% of successful rate of accuracy has been achieved.

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Keywords

  • Pattern recognition
  • invariant features
  • boundaries pixel distance
  • linear function