Simultaneous Separation of Low Level Features in Color Images using Orthogonal Polynomials

Authors

  • Krishnamoorthy Department of IT &CSE ,University college of Engineering-BIT Campus-Tiruchirappalli, India-
  • Thennavan S Department of IT &CSE ,University college of Engineering-BIT Campus-Tiruchirappalli, India-
  • Harini RG Department of IT &CSE ,University college of Engineering-BIT Campus-Tiruchirappalli, India-
  • Narayani KK Department of IT &CSE ,University college of Engineering-BIT Campus-Tiruchirappalli, India-

Keywords:

Edge extraction, Feature extraction, Orthogonal Polynomials Transformation, Textureextraction

Abstract

In this paper, a new method for simultaneous separation of features in color images using Orthogonal Polynomials is proposed. The low-level features,edge and texture present in the color image under analysis are extracted simultaneously in frequency domain usingOrthogonal Polynomials Transformation. The transformed coefficientsobtained from Orthogonal Polynomials Transformation are categorized into color coefficients, texture coefficients and edge coefficients based on the linear contrast due to Orthogonal Polynomials Transformation in different coordinate axes. A Simplified Gradient Measure approach (SGM approach) is used to extract the edge and texture part of the color image from the categorized coefficients simultaneously after careful examination and representation of color textures. The proposed method is tested with various standard color texture images. The results obtained using this proposed feature separation method is encouraging.

References

Chen, LP, Liu, YG, Huang, ZX & Shi, YT, “An improved SOM algorithm and its application to color feature extraction”, Neutral Computing and Applications, Vol. 24, No. 7, pp. 1759-1770,2014.

Ganesan, L, “ An orthogonal polynomials based unified framework for edge detection and texture analysis with its usage in some industrial applications”, Ph. D. Thesis, Department of Computer Science and Engineering, I. I. T., Kharagpur, 1995.

Ganesan, L & Bhattacharya, P, “Edge detection in untextured and textured images: a common computational framework”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 27, No. 5, pp. 823-834, 1997.

Krishnamoorthy R & Bhattacharya, P, “Color edge extraction using orthogonal polynomials based zero crossings scheme”, Information Sciences, Vol. 112, No. 1-4, pp. 51-65, 1998.

Lu, TC & Chang, CC, “ Color image retrieval technique based on color features and image bit map”, Information Processing & Management, Vol.43, No.2, pp.461-472, 2007.

Mohanaiah, Sathyanarayana&Gurukumar, L, “Image texture feature extraction using GLCM approach”, Scientific and Research publications, Vol 3,No 5, pp.290- 294, 2013.

Sanjay Kumar &Ankur Chauhan, “Feature Extraction Techniques based on Color images”, Cloud computing and Big Data, Vol-94, pp.208-214.

Ting-ting Liu, Shuo-zhong Wang, Xin-peng Zhang, Zhiming Yu, “Extraction of color-intensity feature towards image authentication”, Journal of Shanghai University, Vol 14, No.5, pp 337–342, October 2010.

Yushi Chen, hanlu Jiang, Chunyang 2016 “Deep Feature Extraction and Classification of Hyper spectral Images Based on Convolutional Neural Networks”, IEEE Transactions on Geosciences and Remote sensing, Vol. 54, No.10. pp.6232 – 6251.

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Published

2025-11-13

How to Cite

[1]
R. Krishnamoorthy, S. Thennavan, R. Harini, and K. M. Narayani, “Simultaneous Separation of Low Level Features in Color Images using Orthogonal Polynomials”, Int. J. Comp. Sci. Eng., vol. 6, no. 4, pp. 162–166, Nov. 2025.

Issue

Vol. 6 No. 4 (2018): IJCSE Special Issue May Edition

Section

Research Article

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