Features Extraction Based on Linear Regression Technique

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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. © 2012 Science Publications.

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APA

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

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